https://llmobile.kavents.com/tags/open-weights/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.kavents.com/ticker/edge0-35b-iphone-demo/https://llmobile.kavents.com/ticker/edge0-35b-iphone-demo/Thu, 10 Sep 2026 16:08:02 +0200Samuel Zeng has released Edge0, an open-source framework for streaming mixture-of-experts inference, under Apache-2.0. In his launch post he reports a 35-billion-parameter model running on an iPhone at 1 to 2.5 GB of peak memory without a cloud connection. The repository describes three techniques. Expert weights are streamed from storage on demand, so peak memory is bounded by the active set. A trained prerouter predicts expert routing one step ahead so that loads overlap the forward pass, which the project credits with up to 59 percent higher throughput. Recover-LoRA pairs an int4-quantised frozen base with distilled LoRA adapters. Two tiers are published. edge0-35b has 40 layers and 256 experts, a checkpoint of roughly 23 GB and about 2.9 GiB of peak active memory. edge0-8b has 24 layers and 128 experts, a 4.2 GB checkpoint and about 1.0 GB peak. On a Mac mini M4 Pro with 24 GB, the project measures 14.9 to 17.7 tokens per second of decode for the 35B tier and 23.9 to 25.3 for the 8B tier, with prefill at 113 tokens per second cold and 140 warm for 35B. Quality loss against FP16 baselines is given as 3.9 points on average for the 35B tier and 2.8 points for the 8B tier. The repository states that the MLX backend runs on macOS with Apple Silicon, on M1 through M4, and requires Python 3.10 or newer. Your browser does not support this video. This video could not be loaded. Use the link below to open it directly. Demo by Samuel Zeng. Watch original on X Source: https://x.com/SamuelZengML/status/2097861839287927139 Read the article: https://llmobile.kavents.com/ticker/edge0-35b-iphone-demo/iPhoneApple SiliconOpen weightsMixture of expertsMemoryhttps://llmobile.kavents.com/ticker/minicpm5-2b/https://llmobile.kavents.com/ticker/minicpm5-2b/Mon, 07 Sep 2026 10:00:00 +0200OpenBMB has released MiniCPM5-2B, published on Hugging Face and dated 7 September 2026 in the project changelog. The model card describes a dense Transformer “built for on-device, local deployment, and resource-constrained scenarios”, with 2,516,756,480 parameters of which 1,981,982,720 are non-embedding. The architecture uses 42 layers with grouped-query attention at 16 query heads and 2 key-value heads, and supports a context length of 131,072 tokens. It is the second model in the MiniCPM5 series after MiniCPM5-1B, scaling the same recipe to a larger footprint. OpenBMB reports an average score of 53.9 across its evaluation dimensions, including 69.1 on LiveCodeBench, 86.5 on AIME 2025 and 68.1 on NoLiMa. The release is licensed under Apache-2.0 and ships in BF16, GGUF, GPTQ 4-bit, MLX for Apple Silicon and LiteRT-LM for mobile, plus a DSpark draft model for speculative decoding. Source: https://huggingface.co/openbmb/MiniCPM5-2B Read the article: https://llmobile.kavents.com/ticker/minicpm5-2b/OpenBMBOpen weightsQuantisationLiteRThttps://llmobile.kavents.com/ticker/lfm2-5-2-6b/https://llmobile.kavents.com/ticker/lfm2-5-2-6b/Tue, 04 Aug 2026 17:00:00 +0200Liquid AI has released LFM2.5-2.6B, a 2.6-billion-parameter model built on its LFM2 architecture for edge deployment. The company reports 30 tokens per second on a phone, with CPU inference staying under 2.5 GB of memory. Its published chart identifies the device as a Qualcomm Snapdragon Galaxy (SM-S948U1) and puts memory use at 2,448 MB at Q4_K_M quantisation with a 4K-token input context. Chart: Liquid AI. On other CPUs Liquid AI cites 220 tokens per second on an M5 Max and 113 tokens per second on a Ryzen AI Max+ 395. The model was pre-trained on roughly 34 trillion tokens, uses a 128K vocabulary, and its mid-training included a dedicated 128K context-extension phase. Liquid AI positions the model for agentic use, describing planning, tool calling and multi-step tasks, and states that it is competitive with, and often outperforms, models nearly four times its size on instruction-following and tool use, while larger models keep an advantage in coding. Weights are on Hugging Face in BF16, GGUF for llama.cpp, and ONNX. Source: https://www.liquid.ai/blog/lfm2-5-2-6b Read the article: https://llmobile.kavents.com/ticker/lfm2-5-2-6b/Liquid AIOpen weightsLFMBenchmarksAgentshttps://llmobile.kavents.com/ticker/apertus-mini/https://llmobile.kavents.com/ticker/apertus-mini/Wed, 27 May 2026 23:40:00 +0200Researchers working in the Swiss AI Initiative published a technical report on May 27, 2026 describing three language models distilled from Apertus 8B, at 0.5B, 1.5B and 4B parameters. The authors are based at EPFL, ETH Zurich and ISTA, and the models sit on Hugging Face as the Apertus Mini collection. They inherit what the project calls a fully open and compliant release, meaning published weights, published data pipeline and published recipe, with pre-training restricted to openly available data that honours robots.txt opt-outs. The model card lists 1,811 natively supported languages. Each instruction-tuned model also ships in MLX format at 3, 4 and 6 bits, which the authors aimed at Apple devices and at memory-limited mobile and edge deployment. The 4-bit 4B weights are a 2.59 GB file against 7.65 GB for the same model in bfloat16, and the 1.5B and 0.5B come to 1.08 GB and 0.31 GB. The authors build the quantised copies with quantisation-aware distillation, a short recovery run that retrains the compressed weights against the 8B model’s own output distribution, and report 90% to 104% of the unquantised few-shot accuracy on ARC, HellaSwag, MMLU and WinoGrande. Separate FP8 and NVFP4 checkpoints target NVIDIA GPUs instead. The base model table averages ARC, HellaSwag, WinoGrande, XNLI, XCOPA and PIQA, and puts the three sizes at 51.79, 56.66 and 61.53 against 64.96 for the 8B teacher. The same table gives the Qwen3 base models 52.23, 57.51 and 62.14 at comparable sizes, so the distilled models land just under them on the average while leading on the two cross-lingual sets, 45.03 against 43.00 on XNLI and 63.82 against 61.82 on XCOPA at 4B. On a multilingual average of MMLU, TruthfulQA, ARC, instruction following and LogiQA for the instruction-tuned models, the authors report 0.318, 0.382 and 0.473 against 0.401, 0.457 and 0.521 for Qwen3 and 0.497 for Gemma 3 4B, and write that the family is weaker at instruction following and maths. Each model saw 1.7T tokens taken from the final and most heavily filtered phase of the original Apertus data, with no new sources added, packed into 4,096-token sequences and trained on 64 GH200 GPUs at the Swiss National Supercomputing Centre. The cost table puts the 4B run at 2.0E22 FLOPs and the single pass that produced the teacher’s logits for all three students at 1.4E22, against 3.7E23 FLOPs for the original Apertus 8B pre-training on 15T tokens, which the authors describe as making the whole family cost under 12% of the teacher. Swiss AI released 24 checkpoints in total, base, instruction-tuned and quantised, under the Apache 2.0 licence with an acceptable use policy attached, along with the training, post-training and quantisation code. Source: https://arxiv.org/abs/2605.29128 Read the article: https://llmobile.kavents.com/ticker/apertus-mini/Swiss AIDistillationQuantisationOpen weightsApple Siliconhttps://llmobile.kavents.com/ticker/hunyuan-hy-mt-1-5-offline-translation/https://llmobile.kavents.com/ticker/hunyuan-hy-mt-1-5-offline-translation/Wed, 29 Apr 2026 15:55:00 +0200Tencent Hunyuan has open-sourced Hy-MT1.5-1.8B-1.25bit, a translation model that runs fully offline on a phone. Quantisation to 1.25 bits cuts the file from 3.3 GB at FP16 to 440 MB; a 2-bit build is 574 MB. The announcement states the result is 25 percent smaller and around 10 percent faster than prior 1.67-bit approaches with no loss of accuracy. Chart: Tencent Hunyuan. The compression uses Sherry, a ternary quantisation framework with 3:4 fine-grained sparsity: of every four weights, the three most important are stored in 1 bit as -1 or +1 and the fourth is zeroed. A custom STQ kernel aligns the format to the SIMD instruction set of mobile CPUs. Running it requires llama.cpp with STQ1_0 kernel support, added in pull request 22836, and the model card links a side-by-side speed demo recorded on a Snapdragon 888 with 8 GB of RAM. The model covers 33 languages, 5 dialects and minority languages, and 1,056 translation directions, including Tibetan and Mongolian. On the FLORES-200 benchmark Tencent reports it ahead of Tower-Plus-72B, Qwen3-32B, Microsoft Translator and Doubao on Chinese-to-foreign pairs, and the announcement claims it outperforms Google Translate and matches 235B-scale models. Chart: Tencent Hunyuan. Weights are on Hugging Face alongside an Android demo APK for offline translation. The code is released under the AngelSlim licence rather than a standard open-source licence. Source: https://x.com/TencentHunyuan/status/2049487799850840334 Read the article: https://llmobile.kavents.com/ticker/hunyuan-hy-mt-1-5-offline-translation/TencentOpen weightsQuantisationllama.cppTranslationhttps://llmobile.kavents.com/ticker/gemma-4-edge-agent-skills/https://llmobile.kavents.com/ticker/gemma-4-edge-agent-skills/Thu, 02 Apr 2026 18:00:00 +0200Google DeepMind has launched Gemma 4 under the Apache 2.0 licence, and the Google AI Edge team has described how the E2B and E4B variants run on edge hardware. The models support more than 140 languages and a 128K context window. Gemma 4 enables multi-step planning, autonomous action, offline code generation, and even audio-visual processing, all without specialized fine-tuning. Image: Google. The Google AI Edge Gallery app on iOS and Android gains Agent Skills, which Google calls one of the first applications to run multi-step, autonomous agentic workflows entirely on-device. The examples given are querying Wikipedia, turning speech input into charts and flashcards, pairing photos with generated music, and building a small app that plays animal calls through conversation alone. For in-app deployment Google points to LiteRT-LM. It reports that Gemma 4 E2B runs in under 1.5 GB of memory on some devices using 2-bit and 4-bit weights with memory-mapped per-layer embeddings, and that GPU optimisations process 4,000 input tokens across two skills in under 3 seconds. Constrained decoding enforces structured output, and dynamic context lengths let a single model span CPUs and GPUs. On smaller hardware, LiteRT-LM reaches 133 prefill and 7.6 decode tokens per second on a Raspberry Pi 5 CPU, and 3,700 prefill and 31 decode tokens per second on the NPU of a Qualcomm Dragonwing IQ8, the processor behind the Arduino VENTUNO Q. Gemma 4 is available with CPU and GPU support on Android and iOS, system-wide on Android through the new AICore Developer Preview, on Windows, Linux and macOS via Metal, and in the browser through WebGPU. Google also released a Python package and a litert-lm CLI for Linux, macOS and Raspberry Pi, with tool calling included. Source: https://developers.googleblog.com/bring-state-of-the-art-agentic-skills-to-the-edge-with-gemma-4/ Read the article: https://llmobile.kavents.com/ticker/gemma-4-edge-agent-skills/GoogleGemmaAgentsOpen weightsLiteRThttps://llmobile.kavents.com/ticker/qwen-3-5/https://llmobile.kavents.com/ticker/qwen-3-5/Mon, 02 Mar 2026 10:00:00 +0100Alibaba’s Qwen team released Qwen3.5-0.8B and Qwen3.5-2B on March 2, 2026, together with 4B and 9B versions, according to the Qwen release notes. Both are dense vision-language models with a 262,144-token context and Apache 2.0 weights. The model card states that the released files work with Hugging Face Transformers, vLLM, SGLang and KTransformers, and that in light of the parameter scale the intended uses are prototyping, task-specific fine-tuning and other research or development purposes. The benchmark table in that model card puts Qwen3.5-2B at 55.3 on MMLU-Pro with reasoning switched off, where the same table gives 40.2 for last generation’s Qwen3-1.7B and 69.6 for the larger Qwen3-4B-2507, and Qwen3.5-0.8B at 29.7. With reasoning on, Alibaba reports 66.5 for the 2B and 42.3 for the 0.8B on the same test, and 51.6 against 11.9 on GPQA, a set of graduate-level science questions. The same table has Qwen3-1.7B ahead of both new sizes on IFEval, which checks whether a model obeys explicit formatting instructions, at 68.2 against 61.2 for the 2B and 52.1 for the 0.8B with reasoning off. Alibaba ran all of these figures itself. Both sizes ship a vision encoder in the same checkpoint rather than as a separate release, and Alibaba’s vision table gives Qwen3.5-2B 64.2 on MMMU, a college-level image question set, above the 61.4 it lists for the previous Qwen3-VL-2B, with 49 for Qwen3.5-0.8B. The two models stack 24 layers in which three linear-attention blocks, whose memory cost does not grow with the sequence, alternate with one full-attention block. Input embedding and output layer are tied, so a single 248,320-entry matrix does both jobs, and Alibaba puts language coverage at 201 languages and dialects. Alibaba published the weights on Hugging Face and ModelScope under Apache 2.0, and its MNN team put 4-bit exports of both sizes up the same day. The 0.8B build carries a 470 MB language weight file plus 63 MB for the vision encoder, and the 2B build 1.18 GB plus 196 MB, against 1.75 GB and 4.55 GB for the full-precision checkpoints. For other local runtimes the Qwen repository points readers to llama.cpp, which it says supports the series for text and vision, and to mlx-lm and mlx-vlm on Apple silicon, without Alibaba publishing GGUF or MLX builds of these sizes itself. Source: https://huggingface.co/Qwen/Qwen3.5-2B Read the article: https://llmobile.kavents.com/ticker/qwen-3-5/AlibabaQwenOpen weightsQuantisationBenchmarkshttps://llmobile.kavents.com/ticker/mobile-agent-v3-5/https://llmobile.kavents.com/ticker/mobile-agent-v3-5/Sun, 15 Feb 2026 02:52:00 +0100Alibaba’s Tongyi Lab published Mobile-Agent-v3.5 on February 15, 2026, describing GUI-Owl-1.5, a family of agent models that read a screenshot and emit the next tap, type or scroll. The family spans 2B, 4B, 8B, 32B and 235B-A22B parameters in instruct and reasoning variants, all built on Qwen3-VL. Every benchmark in the paper was run server-side against cloud sandboxes and emulators, and the authors give no on-device deployment, latency or hardware figures anywhere, so the small sizes are the only thing that points at a phone. The authors write that the smaller instruct variants, which skip the reasoning step, are fast enough to be deployed on edge devices and to work alongside a larger cloud model. Across platforms, the authors report their 32B-Instruct model reaching 56.5 on OSWorld-Verified for desktop tasks, 47.6 on OSWorld-MCP and 46.8 on Mobile-World, the last two of which also require calling external tools. On AndroidWorld, which scores an agent driving real Android apps in an emulator, their 8B reasoning model reaches 71.6, which the paper puts just behind UI-TARS-2 at 73.3. For browsers, the paper’s table has the 32B reasoning model at 48.4 on WebArena, 46.6 on VisualWebArena and 82.1 on WebVoyager. The small checkpoints carry a large part of that. The authors report 2B-Instruct at 43.5 on OSWorld-Verified and 67.9 on AndroidWorld, and 4B-Instruct at 48.2 and 69.8, which puts the 4B model within 2 points of their own 32B on Android tasks. On ScreenSpot-Pro, which tests whether a model can point at the right element in a high-resolution screenshot, they report 72.9 for 32B-Instruct and 80.3 when the model first crops a region and looks again. The paper’s prose and its tables disagree on which variant owns several headline numbers, and the figures above follow the tables. Charts: Alibaba Tongyi Lab, the authors' own benchmark figures. Six checkpoints are on Hugging Face under the MIT license, covering 2B, 4B and 8B instruct, 8B reasoning, and 32B in both variants. The 235B-A22B model named in the paper is not among them. The code is MIT as well, and the repository points at a hosted cloud-sandbox demo rather than a local runtime. Source: https://arxiv.org/abs/2602.16855 Read the article: https://llmobile.kavents.com/ticker/mobile-agent-v3-5/AlibabaAgentsQwenOpen weightsAndroidhttps://llmobile.kavents.com/ticker/nemotron-flash/https://llmobile.kavents.com/ticker/nemotron-flash/Mon, 24 Nov 2025 09:46:00 +0100Nvidia published Nemotron-Flash on November 24, 2025, a family of small language models built around measured decoding latency instead of parameter count. Nvidia reports Nemotron-Flash-1B decoding 8k tokens in 14.45 seconds on a single Nvidia H100 at batch size 1, against 27.55 seconds for Qwen3-0.6B, while averaging 49.63 across 16 benchmarks to Qwen3-0.6B’s 44.11. The paper was accepted at NeurIPS 2025. The models mix three kinds of layer rather than using attention throughout. Nvidia interleaves DeltaNet and Mamba2 layers, operators that carry a fixed-size state instead of a cache that grows with the context, with a small number of ordinary attention layers, and an evolutionary search picked which operator goes where. Nemotron-Flash-1B holds 0.96B parameters in 12 blocks with only 2 full attention layers, and Nemotron-Flash-3B holds 2.7B parameters in 18 blocks with 3. Nvidia built both wider and shallower than other models of the same size, where Qwen3-0.6B stacks 28 layers, and writes that deep and thin models are parameter efficient but sit off the accuracy-latency frontier. Nemotron-Flash-3B averages 60.98 across the same 16 tasks, which cover MMLU, commonsense reasoning, math, coding and recall, against 58.96 for Qwen2.5-3B and 55.47 for Qwen3-1.7B. On the same H100, with all models running through TensorRT-LLM AutoDeploy kernels and CUDA Graph, Nvidia measured 28.71 seconds for the 3B against 49.40 for Qwen2.5-3B and 36.20 for Qwen3-1.7B, so 1.7 and 1.3 times lower latency. Throughput was measured separately, with a 32k-token input at the largest batch size that still fits in memory, and Nvidia reports 2939 tok/s for the 3B against 459 and 157 for the two Qwen models, and 7289 tok/s for the 1B against 160 for Qwen3-0.6B. Every figure here is Nvidia’s own, taken on data centre GPUs, and the paper gives no phone or embedded measurements, with the earlier depth and operator studies run on an Nvidia A100. Nvidia trained both models on 4.5T tokens using 256 H100 GPUs and released three checkpoints on Hugging Face, Nemotron-Flash-1B, the 3B base model and an instruction-tuned Nemotron-Flash-3B-Instruct, which averages 52.84 across MMLU, GPQA, GSM8K and IFEval against 48.17 for Qwen2.5-1.5B. The model cards set the licence to Creative Commons Attribution-NonCommercial 4.0, so the weights are downloadable but not licensed for commercial use, and neither Apache nor an Nvidia open model licence applies. Running them needs the TensorRT-LLM AutoDeploy attention kernel alongside the Mamba2 and FlashLinearAttention implementations, since Nvidia states that vLLM and TensorRT-LLM do not support DeltaNet-style linear attention on their own. Chart: Fu et al., Figure 1 of the paper. Nvidia's own measurements on an Nvidia H100. Source: https://arxiv.org/abs/2511.18890 Read the article: https://llmobile.kavents.com/ticker/nemotron-flash/NvidiaResearchBenchmarksOpen weightshttps://llmobile.kavents.com/ticker/lfm2/https://llmobile.kavents.com/ticker/lfm2/Thu, 10 Jul 2025 14:00:00 +0200Liquid AI released LFM2 on July 10, 2025, three open-weight checkpoints of 350M, 700M and 1.2B parameters aimed at the CPUs inside phones, laptops and vehicles. The company reports 2x faster decode and prefill speed on CPU than Qwen3, and describes the models as built for millisecond latency and offline operation on the device rather than in a data centre. The architecture is a hybrid of 16 blocks, 10 of them gated short convolutions and 6 grouped query attention. A short convolution mixes each token with a handful of its neighbours instead of attending over the whole history, which is cheap on a phone CPU and keeps the attention cache small, while the six attention blocks preserve the recall that pure convolutions lose. Liquid AI says the layout came out of its STAR architecture search, which it ran against measured peak memory and measured prefill plus decode speed on Qualcomm Snapdragon embedded SoC CPUs rather than against proxy metrics. Liquid AI benchmarked the models on a Samsung Galaxy S24 Ultra with a Qualcomm Snapdragon SoC and on an AMD Ryzen HX370 laptop, using Q4_0 in llama.cpp and 8da4w in ExecuTorch. In its llama.cpp charts LFM2-350M clears 1,300 tokens per second of prefill and runs at roughly 190 tokens per second of decode on the Galaxy S24 at a 128-token sequence, falling to around 120 tokens per second of decode at 4,096 tokens. The company states that LFM2-700M stays faster than Qwen3-0.6B on both decode and prefill in both runtimes despite being 16 percent larger, and that the CPU gains should carry over to GPUs and NPUs once kernels are optimised. Charts: Liquid AI's own llama.cpp measurements at Q4_0 quantisation. The models are small enough to ship whole, with Liquid AI’s own Q4_0 GGUF builds at 219 MB for LFM2-350M and 696 MB for LFM2-1.2B. On Liquid AI’s internal evaluation table LFM2-1.2B scores 55.23 on MMLU and 74.89 on IFEval against 59.11 and 73.98 for Qwen3-1.7B, a model with 47 percent more parameters, while LFM2-700M reaches 49.9 on MMLU against 40.08 for Gemma 3 1B IT. Liquid AI notes that it evaluated Qwen3 in non-reasoning mode only, on the grounds that reasoning traces run past the output budgets that matter on edge devices. Chart: Liquid AI. The average covers MMLU, IFEval, IFBench, GSM8K and MMMLU. Weights are on Hugging Face, with the models also reachable through the Liquid Playground and OpenRouter, and Liquid AI points developers at llama.cpp and ExecuTorch for local testing. The licence is an open licence based on Apache 2.0 that permits academic and research use freely and commercial use by companies under $10 million in revenue, with anyone above that threshold directed to contact the company for a commercial licence. All three checkpoints were trained on 10T tokens, roughly 75 percent English, 20 percent multilingual and 5 percent code, with the context extended to 32K during pre-training. Update, September 23, 2025. Liquid AI added LFM2-2.6B, which widens the stack to 30 blocks, 22 of them convolutions and 8 attention. The company reports 82.41 percent on GSM8K and 79.56 percent on IFEval, and says the model outperforms Llama 3.2-3B-Instruct, Gemma 3 4B IT and SmolLM3-3B. It ships on Hugging Face under the same licence. Update, November 28, 2025. Liquid AI published an LFM2 technical report covering the dense 350M to 2.6B models plus an 8.3B mixture-of-experts variant with 1.5B active parameters, where only part of the model runs per token. Measuring on a Samsung Galaxy S25 with a Snapdragon 8 Elite in llama.cpp at Q4_0, it reports LFM2-350M at 1,067 tokens per second of prefill and 194.1 tokens per second of decode on a 1K-token prompt, and LFM2-2.6B at 143 and 33.8 against 57 and 17.2 for Qwen3-4B. The report names ExecuTorch, llama.cpp and vLLM as the shipped deployment packages. Source: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models Read the article: https://llmobile.kavents.com/ticker/lfm2/Liquid AILFMOpen weightsllama.cppBenchmarkshttps://llmobile.kavents.com/ticker/smollm3/https://llmobile.kavents.com/ticker/smollm3/Tue, 08 Jul 2025 12:30:00 +0200Hugging Face released SmolLM3 on July 8, 2025, a 3B model trained on 11.2 trillion tokens that takes up to 128k tokens of context, covers six languages and answers in either of two modes. It is the successor to SmolLM2, whose largest model was 1.7B with an 8k context window and, in Hugging Face’s own words, primarily understands and generates content in English. The longer context comes from an architecture change plus a separate training stage. Hugging Face removed rotary position embeddings from every fourth layer, a technique it calls NoPE, and replaced multi-head attention with grouped-query attention to shrink the key-value cache during inference. After the main pretraining run it trained the model on another 100 billion tokens in two steps, 4k to 32k and then 32k to 64k, and YaRN, a method for extrapolating past the trained length, carries it to 128k tokens at inference, though the published configuration ships set to 64k and has to be edited to go further. Multilingual text made up 12 percent of the web data at every pretraining stage, and Hugging Face lists English, French, Spanish, German, Italian and Portuguese as supported. Hugging Face’s own base-model figures put SmolLM3 at 76.2 on HellaSwag, 65.6 on ARC and 79.0 on BoolQ, ahead of both Llama 3.2 3B and Qwen2.5 3B and ahead of the larger Qwen3 4B on all three. Qwen3 4B stays in front on maths and code, at 51.2 against 46.1 on MATH and 54.9 against 30.5 on HumanEval+, and on the Ruler 64k long-context test Llama 3.2 3B leads at 72.9 against 67.9 for SmolLM3. On the Flores-200 translation benchmark SmolLM3 scores 56.3 against 55.8 for Qwen3 4B and 54.9 for Qwen2.5 3B, and its multilingual chart shows it ahead of both 3B rivals in all five non-English languages and behind Qwen3 4B in all five. Table: Hugging Face's own base-model figures. A flag in the system prompt, /think or /no_think, decides whether the instruct model reasons before it answers. Hugging Face reports the reasoning mode at 36.7 percent on the AIME 2025 maths competition against 9.3 percent without it, 41.7 against 35.7 on GPQA Diamond and 30.5 against 15.2 on LiveCodeBench v4. Qwen3 4B leads on most of the eight tests in that comparison in both modes, at 58.8 on AIME 2025 and 52.9 on LiveCodeBench, though SmolLM3 without reasoning tops it on the IFEval instruction-following test at 76.7 to 68.9. Table: Hugging Face's own instruct-model figures. Hugging Face published the recipe as well as the weights. Pretraining ran in three stages that moved web data from 85 percent to 75 and then 63 percent while code rose from 12 percent to 24 and maths from 3 percent to 13, each share drawn from named public datasets, and the company released the training configs with the exact data weights along with intermediate checkpoints. Both the base and instruct models are on Hugging Face under the Apache 2.0 license, and the model card names llama.cpp, ONNX, MLX, MLC and ExecuTorch for local inference. The 4-bit GGUF published by ggml-org on release day is 1.92 GB, against 3.27 GB for the 8-bit build for MLX and 6.15 GB for the bfloat16 weights. Diagram: Hugging Face. Source: https://huggingface.co/blog/smollm3 Read the article: https://llmobile.kavents.com/ticker/smollm3/Hugging FaceOpen weightsBenchmarksOpen sourcellama.cpphttps://llmobile.kavents.com/ticker/gemma-3n/https://llmobile.kavents.com/ticker/gemma-3n/Tue, 20 May 2025 19:00:00 +0200Google previewed Gemma 3n on May 20, 2025, a model built for phones, tablets and laptops. Its 5B and 8B versions run with dynamic memory footprints of 2 GB and 3 GB, which Google says matches what 2B and 4B models normally need. The saving comes from Per-Layer Embeddings, a Google DeepMind technique that keeps embedding parameters off the accelerator. For E2B that cuts what has to be loaded into accelerator memory from 5.44B parameters to 1.91B, with the 2.55B embedding parameters cached to fast storage instead. A MatFormer setup nests a smaller but fully functional 2B model inside the 4B one, so developers can trade quality against footprint. Google reports the model starting to respond about 1.5 times faster on mobile than Gemma 3 4B. Gemma 3n takes audio, text, images and video as input, including interleaved inputs across modalities, and handles speech recognition and translation. Google states that the same architecture powers the next generation of Gemini Nano, which was to reach Android and Chrome later in 2025. Qualcomm Technologies, MediaTek and Samsung System LSI worked on the optimisation. Diagram: Google. ▶Announcing Gemma 3n Preview: Powerful, Efficient, Mobile-First AILoading connects your browser to www.youtube-nocookie.com, which may process your IP address and use cookies. Load contentOpen externallyVideo: Google for Developers. Update, June 26, 2025. Google released Gemma 3n as E2B and E4B, naming them for 2B and 4B effective parameters out of 5B and 8B raw ones. Google reports E4B scoring 1303 on LMArena and calls it the first model under 10 billion parameters to pass 1300, ahead of Llama 4 Maverick 17B 128E at 1292 and GPT 4.1-nano at 1288. The release adds a MobileNet-V5-300M vision encoder, which Google says handles up to 60 frames per second on a Pixel, and KV cache sharing that doubles prefill performance. Text covers 140 languages, multimodal understanding 35. Weights are on Hugging Face and Kaggle, with support in Ollama, llama.cpp, MLX, LiteRT and the Google AI Edge Gallery. Chart: Google. Source: https://developers.googleblog.com/en/introducing-gemma-3n/ Read the article: https://llmobile.kavents.com/ticker/gemma-3n/GoogleGemmaGemini NanoAndroidOpen weightshttps://llmobile.kavents.com/ticker/qwen-3/https://llmobile.kavents.com/ticker/qwen-3/Wed, 14 May 2025 15:41:00 +0200Alibaba’s Qwen Team published the Qwen3 Technical Report on May 14, 2025, covering six dense models from 0.6B to 32B parameters and two mixture-of-experts models, where only part of the model is active per token. The two smallest dense sizes, Qwen3-0.6B and Qwen3-1.7B, carry the same mode switch as the rest of the family, so one set of weights either works through a problem step by step before answering or replies straight away. Alibaba reports Qwen3-1.7B scoring 93.4 on MATH-500, a 500-problem maths set, with reasoning on, against 73.0 with it off. Earlier Qwen releases needed two separate models for that, a chat model and a dedicated reasoning model such as QwQ-32B. In Qwen3 the user picks the behaviour with a flag in the prompt or the system message, /think or /no_think, and the report states that reasoning is the default and that across a multi-turn conversation the model follows the last flag it saw. The same section describes a reasoning budget, where the model’s working is cut off once it passes a token limit the user set and it answers from what it has so far, which the authors say they never trained for and which emerged from merging the two modes. The benchmark tables give both small sizes in both modes. For Qwen3-0.6B, Alibaba reports 55.6 on MMLU-Redux, a corrected version of the MMLU knowledge test, with reasoning on against 44.6 with it off, and 77.6 against 55.2 on MATH-500. For Qwen3-1.7B the same pair of tables gives 73.9 and 64.4 on MMLU-Redux, 48.3 against 13.4 on the AIME 2024 competition maths set, and 33.2 against 11.6 on LiveCodeBench v5. Against the reasoning baselines in the report, Qwen3-0.6B is ahead of DeepSeek-R1-Distill-Qwen-1.5B on MMLU-Redux, 55.6 to 45.4, and behind it on MATH-500, 77.6 to 83.9. Alibaba built the small sizes by copying behaviour from the large ones rather than by running the full reinforcement learning pipeline on each. The report describes two stages of distillation from Qwen3-32B and Qwen3-235B-A22B, first on stored teacher outputs generated in both modes, then by matching the small model’s output probabilities against the teacher’s while the small model generates, and it credits the first stage with teaching the smaller models to switch modes at all. Pre-training ran on about 36 trillion tokens covering 119 languages and dialects, up from 29 in Qwen2.5. Context length is where the two small sizes sit apart from the family. Table 1 of the report puts Qwen3-0.6B and Qwen3-1.7B at 32K tokens against 128K for Qwen3-8B and every larger model, and both tie the input embedding to the output layer so one weight matrix does both jobs. All eight models are published under the Apache 2.0 license on Hugging Face, ModelScope and Kaggle. The Qwen3 blog post of April 29, 2025 names Ollama, LM Studio, MLX, llama.cpp and KTransformers as the tools it recommends for running them locally. Source: https://arxiv.org/abs/2505.09388 Read the article: https://llmobile.kavents.com/ticker/qwen-3/AlibabaQwenOpen weightsResearchBenchmarkshttps://llmobile.kavents.com/ticker/huggingsnap/https://llmobile.kavents.com/ticker/huggingsnap/Wed, 19 Mar 2025 16:16:00 +0100Hugging Face published HuggingSnap, an iPhone app that describes what the camera is pointed at, with the model running on the phone rather than in the cloud. The app’s runtime configuration loads SmolVLM2 at 500M parameters in Apple’s MLX format. Hugging Face had trailed the app in its SmolVLM2 announcement on February 20, 2025, writing that the 500M model lets users analyse and understand video content directly on their device with no cloud required. The README describes the app as a way to point the camera and have text translated or summarised, identify plants and animals and learn more about surrounding objects. Inference runs through MLX Swift, Apple’s machine learning framework for Apple silicon, with a modified fork of Apple’s MLX example code supplying the vision-language support. The app pulls the weights from the Hugging Face Hub the first time it runs, then holds Metal memory to 82 percent of what iOS reports as available to the process. Input comes from the live camera, from stills and clips captured in the app, and from photos and videos picked out of the library. A tap on the shutter takes a picture and a press and hold records video, after which the app offers a describe action for images and a summarise action for video, with questions typed or dictated. The app’s own interface states that photos and videos are processed entirely on the device and that no data is sent to the cloud. The README states that installation needs an iPhone running iOS 18, and the Xcode project sets iOS 18.0 as the deployment target and iPhone as the only device family, with no Mac Catalyst or visionOS build. TechCrunch reported the app as a free App Store download on March 19, 2025, quoting the listing as saying it works offline and processes all data on the phone. The SmolVLM2 weights are Apache 2.0, while the app repository itself carries no licence file. Source: https://github.com/huggingface/HuggingSnap Read the article: https://llmobile.kavents.com/ticker/huggingsnap/Hugging FaceiOSiPhoneApple SiliconOpen weightshttps://llmobile.kavents.com/ticker/gemma-3/https://llmobile.kavents.com/ticker/gemma-3/Wed, 12 Mar 2025 17:00:00 +0100Google released Gemma 3 on March 12, 2025 in four sizes, 1B, 4B, 12B and 27B, and describes them as designed to run fast directly on devices, from phones and laptops to workstations. The 1B is a new size for the line, which started at 2B with Gemma and stayed there through Gemma 2. The technical report puts the 1B at 2.0 GB of weights in bfloat16 and at 0.5 GB as a quantised int4 checkpoint, and the 4B at 8.0 GB and 2.6 GB. Context length is not the same across the four. The report gives 128K tokens for the 4B, 12B and 27B and 32K for the 1B, with the three larger models pre-trained at 32K and stretched to 128K at the end of pre-training by rescaling their rotary position embeddings. To stop the key-value cache from swallowing memory at long context, Gemma 3 places five local attention layers between each global one and holds the local layers to a 1024-token window, where Gemma 2 alternated one for one. Google reports that in an ablation on a 2B model a global-attention-only configuration carries a 60 percent memory overhead from the cache at a 32K prefill, and that interleaving with a 1024-token window brings that under 15 percent. Multimodality starts at 4B. The 4B, 12B and 27B share one 400M-parameter SigLIP vision encoder, frozen during training, which takes square images resized to 896 by 896 pixels and condenses each one into 256 tokens for the language model to read. An inference-time step Google calls Pan and Scan cuts non-square or high-resolution images into equal crops and resizes each to the encoder’s resolution, which the report measures as raising the 4B from 72.8 to 81.0 on DocVQA and from 44.1 to 57.0 on InfoVQA in a 4-shot evaluation of a pre-trained checkpoint. The 1B has no vision encoder and takes text only. Google published quantisation-aware trained checkpoints next to the raw weights, made by fine-tuning each model for about 5,000 steps against the probabilities of the unquantised model, so the weights are trained to tolerate the lower precision rather than rounded down afterwards. The three formats are per-channel int4, per-block int4 and switched fp8, which Google picked for what open source inference engines such as llama.cpp already support. The report’s footprint table counts weights alone and weights plus a key-value cache for a 32,768-token sequence, and on the second measure the 1B needs 1.4 GB at per-channel int4 against 2.9 GB in bfloat16, while the 4B needs 7.3 GB against 12.7 GB. Google's own footprint figures for the raw and quantised checkpoints, with and without a key-value cache at 32,768 tokens. Table: Google DeepMind, Gemma 3 technical report, CC BY 4.0. Google reports its own zero-shot figures for the instruction-tuned models and puts Gemma 3 4B at 43.6 on MMLU-Pro and 75.6 on MATH, against 15.6 and 27.2 for Gemma 2 2B and 56.9 and 55.6 for Gemma 2 27B, with the 1B at 14.7 and 48.0. The 4B also scores 48.8 on the MMMU validation set, a benchmark the text-only sizes do not run, and the report calls the instruction-tuned 4B competitive with the instruction-tuned Gemma 2 27B. Google put every size on Hugging Face, Kaggle and Ollama as a pre-trained and an instruction-tuned checkpoint under the Gemma Terms of Use, and names LiteRT, Gemma.cpp, llama.cpp and Google AI Edge among the runtimes that take them. The same release added ShieldGemma 2, a 4B image safety classifier built on Gemma 3 that labels images for dangerous content, sexually explicit material and violence. ▶What's new in Gemma 3?Loading connects your browser to www.youtube-nocookie.com, which may process your IP address and use cookies. Load contentOpen externallyVideo: Google for Developers. Source: https://blog.google/technology/developers/gemma-3/ Read the article: https://llmobile.kavents.com/ticker/gemma-3/GoogleGemmaQuantisationOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/phonelm/https://llmobile.kavents.com/ticker/phonelm/Thu, 07 Nov 2024 03:19:00 +0100Researchers at Beijing University of Posts and Telecommunications published PhoneLM on November 7, 2024, a small language model family in 0.5B and 1.5B sizes built in an unusual order. The team first searched transformer configurations for the fastest inference on a specific phone, then pre-trained the configuration that won, instead of choosing a shape for capability and optimising speed after the fact. PhoneLM-1.5B decodes at 58 tok/s on the CPU of a Xiaomi 14 with a Qualcomm Snapdragon 8 Gen 3. The search varied the layer count from 15 to 25, the ratio of intermediate to hidden size between 2 and 5 and the attention type, and the paper lists measured throughput for seven candidate 1.5B shapes on that phone. The winner has 19 layers and a hidden size of 2,560, a hidden size to layer ratio of 134.7 against the 54.6 to 88.6 the authors observe in recent small models. It uses ReLU rather than the SiLU activation most current small models adopt, which the authors argue is cheaper on NPUs built for integer arithmetic and leaves more zeros in the feed-forward output for sparse-computation tricks. The paper notes that only the 1.5B model went through the search. Across seven zero-shot tasks including HellaSwag, PIQA, BoolQ and both ARC sets, the benchmark table puts PhoneLM-1.5B at an average of 67.31%, ahead of SmolLM-1.7B at 66.49% and StableLM 2 1.6B at 66.34% and behind DCLM-1.4B at 68.33% and Gemma 2 2B at 71.31%. PhoneLM-0.5B averages 60.43% against 60.17% for SmolLM-360M and 56.46% for OpenELM-450M. The authors measure PhoneLM-1.5B decoding 1.2 times faster than StableLM 2 1.6B and 1.6 times faster than SmolLM 1.7B on the same handset. The two models saw 1.1T and 1.5T training tokens, all of them from open datasets such as DCLM-baseline, StarCoderData and Dolma. The group ran every device measurement through its own inference engine mllm, with the linear and embedding weights quantised to 4-bit and four threads pinned to the phone’s performance cores. On the NPU it goes through Qualcomm’s QNN framework, where the authors report prompt processing for PhoneLM-1.5B at 654 tok/s on the Xiaomi 14 against 602 tok/s for Qwen 2.5 1.5B. The 4-bit mllm build the group publishes is a 1.41 GB file. The paper states no peak memory figure. An Android demo app on top of mllm runs PhoneLM-1.5B locally for chat and for Android intent invocation, so a user who asks to be woken at 8 o’clock has an alarm set through the system’s own alarm intent. That variant, PhoneLM-1.5B-Call, was fine-tuned with LoRA on DroidCall, 10,000 synthetic function-calling examples covering alarms, timers, email drafts and searches, and the paper places it above GPT-4o-mini, which scores 71.0% on the same test set. Weights are on Hugging Face under Apache 2.0 and the data preparation, training, fine-tuning and evaluation code is on GitHub under GPL-2.0, alongside the list of open datasets used at each stage. Source: https://arxiv.org/abs/2411.05046 Read the article: https://llmobile.kavents.com/ticker/phonelm/ResearchOpen weightsAndroidNPUAgentshttps://llmobile.kavents.com/ticker/smollm2/https://llmobile.kavents.com/ticker/smollm2/Thu, 31 Oct 2024 19:30:00 +0100Hugging Face released SmolLM2 on October 31, 2024, a second generation of its small language models at 135M, 360M and 1.7B parameters. The company trained the 1.7B model on 11 trillion tokens, against 1T for the first SmolLM at the same size, and its model cards put the 360M model at 4T tokens and the 135M at 2T. The mixture combines FineWeb-Edu, DCLM and The Stack with datasets Hugging Face curated for this run, which its paper from February 2025 names as FineMath for mathematics, Stack-Edu for code and SmolTalk for instruction tuning. That paper states the 1.7B run used a multi-stage schedule that held the highest quality maths and code data back for the final tenth of training, from 10T to 11T tokens, while the 360M and 135M models trained in a single stage on a mix kept consistent throughout. Hugging Face fine-tuned the instruct versions on SmolTalk and then tuned them further on ranked answer pairs from UltraFeedback. Hugging Face reports the 1.7B base model at 60.5 on ARC against 49.2 for Llama 3.2 1B and 58.5 for Qwen2.5 1.5B, 36.7 on TriviaQA against 28.1 and 20.9, and 19.4 on MMLU-Pro against 11.7 and 13.7, all run zero-shot with its own lighteval harness and printed in the model card. The instruct version scores 56.7 on the IFEval instruction-following test against 53.5 for Llama 3.2 1B Instruct and 47.4 for Qwen2.5 1.5B Instruct, while Qwen2.5 stays ahead on the MT-Bench chat test at 6.52 to 6.13. At the smaller sizes Hugging Face compares the 360M model with Qwen2.5 0.5B, reporting 53.0 against 45.4 on ARC and 16.9 against 4.3 on TriviaQA, and publishes no third-party comparison for the 135M model. Table: Hugging Face's own figures for the base models. The weight files run 269 MB for the 135M model, 724 MB for the 360M and 3.42 GB for the 1.7B in bfloat16, and Hugging Face uploaded 4-bit ONNX builds the same day at 118 MB, 273 MB and 1.11 GB, plus a 4-bit GGUF of the 1.7B instruct model at 1.06 GB. The repository says the models run locally through llama.cpp, MLX, MLC and transformers.js. All six checkpoints carry an 8k context window and the Apache 2.0 license. Update, November 27, 2024. Hugging Face published a browser chat demo that runs the 1.7B instruct model through transformers.js and ONNX Runtime Web on WebGPU, with no server call after the download. Its local inference guide adds runnable code for llama.cpp, MLX and MLC, and lists the models on Ollama and in the PocketPal-AI phone app. Source: https://github.com/huggingface/smollm Read the article: https://llmobile.kavents.com/ticker/smollm2/Open weightsBenchmarksOpen sourcellama.cppWebGPUhttps://llmobile.kavents.com/ticker/amd-llama-135m/https://llmobile.kavents.com/ticker/amd-llama-135m/Fri, 27 Sep 2024 19:00:00 +0200AMD released AMD-Llama-135M on September 27, 2024, its first small language model, at 135M parameters and trained from scratch on AMD’s own accelerators. AMD states that pretraining used 670B tokens drawn from SlimPajama and Project Gutenberg and took six days on four Instinct MI250 nodes holding four MI250 accelerators each. A second variant, AMD-Llama-135M-code, adds 20B tokens from the Python subset of StarCoder over another four days on the same hardware. That code variant exists to work as a draft model for speculative decoding, where a small model guesses several tokens in one pass and a larger model then checks them, so the larger model can accept several tokens per forward pass rather than producing one at a time. AMD paired it with Meta’s CodeLlama-7b and measured throughput on the HumanEval coding benchmark. AMD reports about 2.8x higher throughput on an Instinct MI250 accelerator, about 3.88x on the CPU of a Ryzen AI processor, and about 2.98x with the 7B model running 4-bit on the Ryzen AI NPU while the draft model ran on the CPU, each against the same setup without speculative decoding. AMD’s footnotes name a Ryzen 9 PRO 7940HS as the test machine for the Ryzen AI numbers. Diagram: AMD, showing its own measured speedups. AMD published the weights on Hugging Face and the training, dataset and inference code on GitHub, under the Apache 2.0 license. AMD states the training code builds on TinyLlama and uses PyTorch FSDP for distributed training across nodes. Source: https://www.amd.com/en/developer/resources/technical-articles/introducing-amd-first-slm-135m-model-fuels-ai-advancements.html Read the article: https://llmobile.kavents.com/ticker/amd-llama-135m/AMDLlamaNPUOpen weightsOpen sourcehttps://llmobile.kavents.com/ticker/llama-3-2-1b-3b/https://llmobile.kavents.com/ticker/llama-3-2-1b-3b/Wed, 25 Sep 2024 19:00:00 +0200Meta released Llama 3.2 on September 25, 2024, including text-only 1B and 3B models built for phones and edge hardware. Both carry a 128K token context window and are aimed at summarisation, instruction following and rewriting that run locally, with the data staying on the device. Meta built them by structured pruning from Llama 3.1 8B, then recovered quality through knowledge distillation using logits from the 8B and 70B models during pretraining. The company reports the 3B model ahead of Gemma 2 2.6B and Phi 3.5-mini on instruction following, summarisation, prompt rewriting and tool use, and puts the 1B model on a par with Gemma. In Meta’s own table the 3B model scores 77.4 on IFEval against 61.9 for Gemma 2 2B and 59.2 for Phi-3.5-mini, and 67.0 on BFCL V2 for tool use against 27.4 and 58.4. Table: Meta. The company measured the Gemma and Phi results itself. The models shipped with day-one support for Qualcomm and MediaTek silicon and run on Arm, which Meta says covers 99 percent of mobile devices. Weights are on llama.com and Hugging Face, with deployment paths through PyTorch ExecuTorch for devices and Ollama for single-node setups, and the company lists more than 25 partner platforms at launch. Source: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ Read the article: https://llmobile.kavents.com/ticker/llama-3-2-1b-3b/MetaLlamaOpen weightsQualcommMediaTekhttps://llmobile.kavents.com/ticker/olmoe/https://llmobile.kavents.com/ticker/olmoe/Tue, 03 Sep 2024 19:08:20 +0200Ai2 published OLMoE-1B-7B on September 3, 2024, a language model with 6.9B total parameters of which only 1.3B run on any given token. It is a sparse mixture of experts, meaning each layer holds 64 small feedforward networks and a small router picks 8 of them per token, so the rest of the model stays idle. Compute per token lands near that of a dense 1B model, while all 6.9B parameters still have to be held in memory. Ai2 pretrained the model on 5 trillion tokens and reports in the paper that it leads every open model in its active-parameter class. On MMLU the paper puts OLMoE-1B-7B at 54.1, against 48.5 for DCLM-1B, 33.6 for TinyLlama-1B and 32.1 for Ai2’s own dense OLMo-1B. The paper states the model matches or outperforms the dense OLMo-7B at the end of pretraining while using less than half the training FLOPs, and that it beats Llama2-7B on MMLU, scored at 46.2, despite needing roughly 6 to 7 times less compute per forward pass. The instruction-tuned version is reported above Llama2-13B-Chat and DeepSeekMoE-16B. For the training-cost claim the authors ran a controlled comparison, training a 1.3B dense model and a 1.3B active, 6.9B total mixture of experts on 128 H100 GPUs for 130B tokens each. Ai2 reports the sparse model reaching the dense model’s final quality with about 3 times fewer tokens and FLOPs, but only about 2 times faster in wall-clock time, because the larger weight set dropped throughput to 23,600 tokens per second per GPU against 37,500 for the dense run. The full pretraining run used 256 H100 GPUs for around 10 days. Ai2 shipped an iOS app on February 11, 2025 that keeps the whole model on the device, so prompts and responses never reach a server and generation keeps working in airplane mode. Ai2 names the iPhone 15 Pro and 15 Pro Max, the four iPhone 16 models and M-series iPads going back to the 2021 iPad Pro, and states that the first version needs an iOS device with 8 GB of memory. The app runs a 4-bit quantised build of the January 2025 instruction-tuned model, which Ai2 measures at 41 tokens per second on average on an iPhone 16 Pro, on a stack Ai2 built with GenUI on top of llama.cpp. Weights, the training data mix, the training code and the run logs are all published under Apache 2.0, together with 244 intermediate checkpoints. The comparison table in the paper lists OLMoE as the only mixture-of-experts release that opens all four, with most of the other models in the table offering a single checkpoint and no logs. The app is in the App Store and its source code is on GitHub. Screenshot: Ai2. Update, April 14, 2025. Ai2 kept working on the app after the February launch and added macOS support on March 7, 2025 through Mac Catalyst, Apple’s compatibility layer that runs the same iOS code natively on a Mac, together with a counter that shows how many tokens a reply used. A later version reworked the panel that reports generation metrics, and the most recent release, from April 14, 2025, renamed the app to Ai2 OLMoE. Nothing has shipped in the repository since. Source: https://arxiv.org/abs/2409.02060 Read the article: https://llmobile.kavents.com/ticker/olmoe/Ai2Mixture of expertsiOSOpen weightsResearchhttps://llmobile.kavents.com/ticker/gemma-2/https://llmobile.kavents.com/ticker/gemma-2/Wed, 31 Jul 2024 17:00:00 +0200Google released Gemma 2 2B on July 31, 2024, an open model of 2.6B parameters that Google says produces outsized results by learning from larger models through distillation. Google reports it at 1126 Elo on the LMSYS Chatbot Arena leaderboard, captured on July 30, 2024, ahead of Mixtral 8x7B Instruct at 1114, GPT 3.5 Turbo 0314 at 1106 and Llama 2 70B chat at 1093, and states that the model surpasses all GPT-3.5 models there. Chart: Google, from the announcement. Scores are Google's own reading of the leaderboard on July 30, 2024. The technical report describes training the 2B size on 2 trillion tokens with knowledge distillation, where a bigger teacher model supplies a probability for every token in context and the small student is trained to match those probabilities rather than to predict the next token. In an ablation the authors ran, a 2B model trained over 500B tokens distilled from a 7B teacher averaged 67.7 across three benchmarks, against 60.3 for the same model trained from scratch. The released model holds 2.02B non-embedding parameters plus 590M embedding parameters, which come from the 256k-entry Gemini vocabulary, and it interleaves local sliding-window attention with global attention over an 8192-token context. Google DeepMind trained it on 512 TPUv5e chips. Google names edge devices and laptops as deployment targets next to cloud serving on Vertex AI and Google Kubernetes Engine, and says the model is small enough for the free tier of T4 GPUs in Google Colab. For running it locally Google points to Ollama and to Gemma.cpp, its own lightweight C++ inference engine, and says support in MediaPipe, the on-device inference stack for Android, iOS and the web, is coming. Google adds that NVIDIA optimised the model with the TensorRT-LLM library for GeForce RTX cards and for Jetson modules at the edge. Google put the weights on Kaggle, Hugging Face and Vertex AI Model Garden under the Gemma terms, which it calls commercially friendly for research and commercial applications, and made the model available to try in Google AI Studio. The same announcement added ShieldGemma, a set of safety classifiers built on Gemma 2 that filter hate speech, harassment, sexually explicit and dangerous content, and Gemma Scope, over 400 sparse autoencoders for inspecting the inner workings of Gemma 2 2B and 9B. Source: https://developers.googleblog.com/en/smaller-safer-more-transparent-advancing-responsible-ai-with-gemma/ Read the article: https://llmobile.kavents.com/ticker/gemma-2/GoogleGemmaDistillationOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/spectra/https://llmobile.kavents.com/ticker/spectra/Wed, 17 Jul 2024 07:53:00 +0200Nolano AI, the University of Montreal and Mila published Spectra on July 17, 2024, a suite of 54 language models covering nine parameter counts from 99M to 3.9B, each trained on the same 300B tokens of SlimPajama. Every size exists as a half-precision model the authors call FloatLM, as GPTQ-quantised copies at 3, 4, 6 and 8 bits called QuantLMs, and as a TriLM, a model trained from scratch with every linear-layer weight restricted to -1, 0 or 1. The paper’s size table puts TriLM 3.9B at 10.76 billion bits, roughly 1.3 GB, against 63.83 billion bits for the half-precision model of the same parameter count, a factor of 5.9. That is the comparison the suite is built for, since what fits on a device is bits rather than parameters. Averaged over six commonsense and reasoning benchmarks, the paper’s tables put TriLM 3.9B at 60.7 against 61.4 for FloatLM 3.9B, level with the 4-bit QuantLM 3.9B at 60.7 which needs 20.59 billion bits. Set against the models nearest to it in size, TriLM 3.9B beats the half-precision 830M model, 60.7 against 53.3 at 13.34 billion bits, and the 3-bit QuantLM 2.4B, 60.7 against 54.7 at 10.95 billion bits. On LAMBADA it reaches 61.6 against 61.1 for FloatLM 3.9B, and on TriviaQA 21.3 against 21.5. Chart: Kaushal et al. Averages across Arc Easy, Arc Challenge, BoolQ, HellaSwag, PIQA and WinoGrande. During training a TriLM keeps floating-point latent weights and ternarises them on the fly in the forward pass, with one scale value per weight matrix shard and gradients passed through by a straight-through estimator, so only the ternary states and the scales are needed at inference. Embeddings and the output head stay in half precision across the suite. The authors position TriLM against BitNet b1.58, which also trains ternary weights from scratch, and say they normalise once before each linear layer instead of normalising, scaling and quantising activations to 8 bits four to seven times per transformer block; their own replication of the BitNet architecture at 1.1B, trained on 100B tokens, scores below TriLM 1.1B on the same benchmarks. The paper names the weaknesses it measured. At 1.1B parameters the ternary model does not reach its half-precision counterpart, 51.6 against 54.9 on the six-benchmark average, and the 2.4B one still trails at 57.3 against 59.2, so the match with a half-precision model of the same parameter count holds at 3.9B only. On CrowsPairs, which measures how often a model prefers a stereotyped sentence, TriLM 3.9B lands at 65.4 percent against 64.7 for FloatLM 3.9B and 62.6 for the smaller FloatLM 830M, so the ternary model stereotypes more than the half-precision model closest to it in bits. No model in the suite beats random guessing on TruthfulQA, and while TriLM 3.9B holds up on Penn Treebank and LAMBADA perplexity, it stays behind FloatLM 3.9B on web corpora including Dolma, C4 and RefinedWeb, which the authors read as weaker memorisation of training data. The weights and more than 500 intermediate checkpoints are on Hugging Face under Apache 2.0. The TriLM checkpoints are published unpacked to FP16, which the model cards say makes them compatible with ordinary half-precision matrix multiplication and leaves the released files at full half-precision size. The authors list smartphones, laptops and automobiles among the resource-constrained devices they see for ternary models, and end by calling for work on the toxicity, stereotyping and web-corpus gaps they measured. Source: https://arxiv.org/abs/2407.12327 Read the article: https://llmobile.kavents.com/ticker/spectra/ResearchQuantisationMemoryOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/smollm/https://llmobile.kavents.com/ticker/smollm/Tue, 16 Jul 2024 16:00:00 +0200Hugging Face released SmolLM on July 16, 2024, a family of three base models at 135M, 360M and 1.7B parameters. The team says the models are designed to be small enough to run locally across a range of hardware, from smartphones to laptops, and cites the iPhone 15 with 6 GB of DRAM and the iPhone 15 Pro with 8 GB as reference points. Hugging Face trained the 135M and 360M models on 600B tokens each and the 1.7B model on 1T tokens. The substance is the training data, released as SmolLM-Corpus. Cosmopedia v2 contributes 28B tokens of synthetic textbooks, stories and articles generated by Mixtral-8x7B-Instruct, 39 million documents built from a predefined list of 34,000 topics taken from the BISAC book classification instead of from unsupervised clustering. FineWeb-Edu contributes 220B deduplicated tokens of educational web pages, pulled out of FineWeb by a quality classifier trained on annotations from Llama3-70B-Instruct. Python-Edu applies the same idea to code, keeping 4B tokens of the 40B Python tokens in The Stack that scored 4 or higher on an educational code classifier. On Hugging Face’s own evaluation, run with the same lighteval setup for every model it could download, SmolLM-135M averages 39.50 across MMLU, TriviaQA, ARC, PIQA, HellaSwag, OpenBookQA and Winogrande, against 32.75 for GPT2-137M and 32.49 for Pythia-160M. SmolLM-360M averages 44.44 against 39.60 for Qwen2-500M and 39.32 for Qwen1.5-500M, and SmolLM-1.7B averages 51.97 against 50.36 for Microsoft’s Phi-1.5 and 47.98 for Qwen2-1.5B. Meta’s MobileLLM is not publicly available, so Hugging Face took its per-benchmark numbers from the paper and reports no average for those rows. Table: Hugging Face's own figures, evaluated with the same setup for every model except MobileLLM. Hugging Face also benchmarked the memory footprint of each size. SmolLM-135M takes 269.03 MB in bf16, 162.87 MB at int8 and 109.78 MB at int4. SmolLM-360M takes 723.65 MB, 409.07 MB and 251.79 MB across the same three precisions, and SmolLM-1.7B takes 3422.76 MB, 1812.14 MB and 1006.84 MB. Table: Hugging Face. The weights are on Hugging Face under Apache-2.0, with ONNX checkpoints published beside the transformers ones and a GGUF build for llama.cpp announced as planned. Hugging Face also put up browser demos of the instruction-tuned 135M and 360M models that run client-side through WebGPU, with no server call. The corpus is released separately under ODC-By. Source: https://huggingface.co/blog/smollm Read the article: https://llmobile.kavents.com/ticker/smollm/Hugging FaceOpen weightsBenchmarksMemoryWebGPUhttps://llmobile.kavents.com/ticker/qwen2/https://llmobile.kavents.com/ticker/qwen2/Mon, 15 Jul 2024 14:35:00 +0200Alibaba’s Qwen Team published the Qwen2 Technical Report on July 15, 2024, covering five models from 0.5B to 72B parameters. The report states that the two smallest, Qwen2-0.5B and Qwen2-1.5B, are designed for easy deployment on portable devices such as smartphones, earphones and smart glasses, while the larger sizes target GPUs. Qwen2-1.5B scores 56.5 on MMLU, a multiple-choice knowledge test, against 52.7 for Microsoft’s Phi-2, which the same table credits with about twice the non-embedding parameters. Both small sizes tie the input embedding to the output layer, so a single weight matrix does both jobs and the shared vocabulary of 151,646 tokens is stored once rather than twice. The report puts non-embedding parameters at 0.3B for Qwen2-0.5B and 1.2B for Qwen2-1.5B. All five models use grouped query attention, where several query heads share one set of key and value heads, with 14 query heads over 2 key-value heads at 0.5B and 12 over 2 at 1.5B, which the authors say lowers the key-value cache size per token against Qwen1.5 and with it the memory footprint during long-context inference. Alibaba trained Qwen2-0.5B on a 12 trillion token dataset and the other dense models on a 7 trillion token one, reporting that the larger corpus did not pay off at the bigger sizes. For the instruction-tuned versions the report gives Qwen2-1.5B-Instruct 52.4 on MMLU, 61.6 on the GSM8K grade-school maths set and 29.0 on IFEval, which checks whether a model obeys explicit formatting instructions, against 37.9, 40.1 and 20.0 for Qwen2-0.5B-Instruct. The authors attribute the gain over the similarly sized Qwen1.5 models mainly to that data scaling. Context length differs across the family. The authors tested retrieval from long documents with Needle in a Haystack and report that the two small models handle 32K tokens, Qwen2-57B-A14B-Instruct 64K, and the 7B and 72B instruction-tuned models 128K with the YARN extension applied. The pre-training data covers approximately 30 languages, among them English, Chinese, Spanish, French, German, Arabic, Russian, Korean, Japanese, Thai and Vietnamese. Alibaba put the weights on Hugging Face and ModelScope. The Qwen2 blog post of June 7, 2024 states that Qwen2-0.5B, 1.5B, 7B and 57B-A14B carry the Apache 2.0 license while Qwen2-72B and its instruction-tuned variant stay on Alibaba’s own Qianwen License, and it names MLX, llama.cpp, Ollama and LM Studio among the projects that run the models locally. Source: https://arxiv.org/abs/2407.10671 Read the article: https://llmobile.kavents.com/ticker/qwen2/AlibabaQwenOpen weightsResearchWearableshttps://llmobile.kavents.com/ticker/mobileclip/https://llmobile.kavents.com/ticker/mobileclip/Fri, 21 Jun 2024 02:15:00 +0200Five Apple researchers presented MobileCLIP at CVPR 2024 on June 20, a family of image-text models built for phones. They exported each model with Core ML Tools 7.0 and timed it at batch size 1 on an iPhone 12 Pro Max running iOS 17.0.3, and their Table 7 gives the smallest variant, MobileCLIP-S0, 1.5 ms for the image encoder and 1.6 ms for the text encoder at 67.8% zero-shot accuracy on ImageNet. The same authors measure OpenAI’s ViT-B/16 CLIP on that phone at 11.5 ms and 3.3 ms for 68.3%, and both models average 58.1 across the 38 datasets of the DataComp evaluation suite. Multi-modal reinforced training, the method the paper introduces, moves the expensive part of distillation out of the training loop and into the dataset. Apple reinforces the DataComp dataset once, writing five synthetic captions per image with the CoCa model in OpenCLIP, storing the parameters of strong random image augmentations, 30 per image for the 12M subset and 10 for the billion-sample set, and storing the embeddings that two ViT-L/14 teacher models produce for those augmented images and for the real and synthetic captions. Training afterwards reads the captions and the teacher embeddings from disk, so the captioning model and the teachers never run again however many models are trained on the set. The paper puts numbers on what that saves. One epoch over the 12.8M samples of the reinforced set on a single node of 8 A100-80GB GPUs takes 1.3 hours, the same as an epoch on the unreinforced data, against 21.1 hours when the captioning model and the teacher ensemble run during training, according to Table 4d. The cost moves to storage, and Table 4c puts DataComp-1B at 90 TB against 140 TB for the reinforced DataCompDR-1B, with the 12M subset going from 0.9 TB to 1.9 TB. MobileCLIP comes in four sizes, and Table 7 lists image and text encoder parameters separately. S0 pairs an 11.4M-parameter image encoder with a 42.4M-parameter text encoder, S1 uses 21.5M and 63.4M at 2.5 ms and 3.3 ms, S2 uses 35.7M and 63.4M at 3.6 ms and 3.3 ms, and MobileCLIP-B keeps a standard ViT-B/16 image encoder at 86.3M and 10.4 ms. Zero-shot ImageNet accuracy across the four runs 67.8%, 72.6%, 74.4% and 76.8%, and a version of B trained on 39B seen samples instead of 13B reaches 77.2%. All three small variants replace the vision transformer with a convolution-transformer hybrid derived from Apple’s FastViT, and S0 also swaps the text encoder for one that mixes 1-D convolutions with four self-attention layers, which the authors report as 5% smaller and 15.8% faster than the 12-layer transformer text encoder that S1 and S2 keep. Apple released the code, the weights and the reinforced data at apple/ml-mobileclip, with checkpoints for all four variants and the longer-trained B, and with DataCompDR-1B and DataCompDR-12M on Hugging Face as synthetic captions, augmentation parameters and teacher embeddings keyed to DataComp images rather than as the images themselves. The repository lists the code under MIT, the models under Apple’s own machine learning research model terms, and the data under CC BY-NC-ND. Apple showed zero-shot scene classification running in real time on an iPhone in the CVPR exhibit hall, and added an iOS app for the same task to the repository in November 2024. Source: https://openaccess.thecvf.com/content/CVPR2024/html/Vasu_MobileCLIP_Fast_Image-Text_Models_through_Multi-Modal_Reinforced_Training_CVPR_2024_paper.html Read the article: https://llmobile.kavents.com/ticker/mobileclip/AppleiPhoneResearchBenchmarksOpen weightshttps://llmobile.kavents.com/ticker/fox/https://llmobile.kavents.com/ticker/fox/Thu, 13 Jun 2024 15:00:00 +0200TensorOpera released Fox-1 on June 13, 2024, a decoder-only language model with 1.6B parameters trained on 3 trillion tokens of text and code. The company says the run followed a three-stage data curriculum at 8K sequence length, without saying what each of the three stages contained. Fox-1 uses grouped query attention, where several attention heads share one set of key and value vectors to cut memory traffic, with 16 attention heads over 4 key-value heads. TensorOpera describes the model as deeper than others of its size, 78% deeper than Gemma-2B, 33% deeper than Qwen1.5-1.8B and StableLM-2-1.6B, and 15% deeper than Apple’s OpenELM-1.1B. TensorOpera reports an average of 47.13% across ARC Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande and GSM8k, which it ran in the Open LLM Leaderboard setup on one machine with 8 H100 GPUs. It puts Qwen1.5-1.8B at 46.81%, Gemma-2B at 46.36%, StableLM-2-1.6B at 45.92% and OpenELM-1.1B at 38.28%. Fox-1 leads that group on GSM8k with 36.39% against 34.04% for Qwen1.5-1.8B and 17.06% for Gemma-2B, and trails Qwen1.5-1.8B on MMLU with 43.05% against 47.15%. Chart: TensorOpera's own figures. The device claim in the announcement is a target rather than a measurement. TensorOpera puts the model’s FP16 memory use at 3,703 MiB, against 5,379 MiB for Gemma-2B, 4,739 MiB for Qwen1.5-1.8B and 3,852 MiB for StableLM-2-1.6B, and says that makes Fox-1 comparable for on-device deployment. The stated plan is that developers train on the company’s cloud platform and then deploy, personalise and monitor models on smartphones and AI-enabled PCs through its FedML platform. No figure for speed or latency on a phone appears in the post, and the throughput number it does give, over 200 output tokens per second per user, was measured with vLLM on a single H100. Chart: TensorOpera's own figures. TensorOpera published the base model under the Apache 2.0 license on Hugging Face and on its own platform, and said an instruction-tuned version would follow. Source: https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants/ Read the article: https://llmobile.kavents.com/ticker/fox/TensorOperaOpen weightsBenchmarksMemoryhttps://llmobile.kavents.com/ticker/openelm/https://llmobile.kavents.com/ticker/openelm/Tue, 23 Apr 2024 01:12:00 +0200Apple posted OpenELM to arXiv on April 22, 2024, a family of four language models at 270M, 450M, 1.1B and 3B parameters, each also released in an instruction-tuned version. The name stands for Open-source Efficient Language Models, and the efficiency comes from layer-wise scaling, which spreads the parameter budget unevenly across the transformer’s layers instead of giving every layer the same shape. Table 1 of the paper gives the 1.1B model an average of 45.93% across the OpenLLM leaderboard tasks against 43.57% for OLMo 1.2B, a gap the authors state as 2.36%, reached on 1.5T pre-training tokens against OLMo’s 3.0T. Layer-wise scaling varies the number of attention heads and the width of the feed-forward network from layer to layer, keeping layers near the input narrower and widening them towards the output, so a layer late in the stack holds more parameters than an early one. Apple trained the four variants for 350k iterations with its CoreNet library on public data only, drawing on RefinedWeb, a deduplicated PILE, a subset of RedPajama and a subset of Dolma v1.6, about 1.8T tokens in total. Text is filtered and tokenised on the fly rather than pre-tokenised, using the same tokeniser as Llama. The paper benchmarks the models on an Apple MacBook Pro with an M2 Max chip and 64 GiB of RAM running macOS 14.4.1, through a port of OpenELM to MLX v0.10.0, and separately on a Linux workstation with an Intel i9-13900KF and an NVIDIA RTX 4090. On the MacBook in BFloat16, Apple measures the 270M model generating 212.40 tok/s and the 3B model 33.96 tok/s, with the 4-bit versions at 256.35 and 60.33 tok/s. On the CUDA machine OpenELM 1.08B is slower than OLMo 1.18B, at 92.15 tok/s against 203.40, which the authors attribute to a naive RMSNorm implementation and to OpenELM having 113 normalisation layers where OLMo has 33. Apple presents the release as the complete framework rather than weights and inference code, covering the training and evaluation code for public datasets, the training logs, multiple checkpoints, the pre-training configurations and the code that converts a model to MLX for inference and fine-tuning on Apple devices, with pre-converted bf16 and 4-bit checkpoints for all four sizes. The eight models are on Hugging Face and the code is in CoreNet. Both the model cards and CoreNet name the terms as the Apple sample code license, which is Apple’s own license and not one of the standard open source licenses. Source: https://arxiv.org/abs/2404.14619 Read the article: https://llmobile.kavents.com/ticker/openelm/AppleApple SiliconResearchOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/phi-3-mini/https://llmobile.kavents.com/ticker/phi-3-mini/Mon, 22 Apr 2024 18:00:00 +0200Microsoft introduced Phi-3-mini on April 22, 2024, a 3.8-billion-parameter model trained on 3.3 trillion tokens. The technical report puts it at 69 percent on MMLU and 8.38 on MT-bench, which the authors say rivals models such as Mixtral 8x7B and GPT-3.5. Quantised to 4 bits the model occupies about 1.8 GB, small enough for a phone. The report describes the test directly. We tested the quantized model by deploying phi-3-mini on iPhone 14 with A16 Bionic chip running natively on-device and fully offline achieving more than 12 tokens per second. Screenshots: Microsoft, Figure 1 of the technical report. The same report covers two larger siblings, Phi-3-small at 7B parameters and Phi-3-medium at 14B, both trained on 4.8 trillion tokens. Microsoft reports 75 percent on MMLU for the small model and 78 percent for the medium one. Source: https://arxiv.org/abs/2404.14219 Read the article: https://llmobile.kavents.com/ticker/phi-3-mini/MicrosoftPhiiPhoneQuantisationOpen weightshttps://llmobile.kavents.com/ticker/stable-lm-2-zephyr/https://llmobile.kavents.com/ticker/stable-lm-2-zephyr/Tue, 27 Feb 2024 20:00:00 +0100Stability AI published the Stable LM 2 1.6B technical report on February 27, 2024, describing a decoder-only model of 1.6B parameters trained from scratch on roughly 2 trillion tokens of public data. The mix is multilingual by design, with web text in German, Spanish, French, Italian, Dutch and Portuguese alongside English, and the report states the finished model handles all seven. The model has 24 layers and a context length of 4096 tokens, which the authors list as one of the things they want to extend. In the report’s multilingual table, which averages translated versions of four English benchmarks and a next-word prediction set across those seven languages, the base model scores 40.5, above Google’s Gemma 2B at 39.8 with 2.5B parameters and Microsoft’s Phi-2 at 34.6 with 2.7B. The chat variant reaches 41.5 and Stability’s own 3B model 41.7. The authors state they used no multilingual data during fine-tuning, so the language coverage comes out of pre-training alone. On the six English tasks of the Hugging Face Open LLM Leaderboard, the same report’s table puts the base model at an average of 45.3, behind Alibaba’s Qwen 1.5 1.8B at 46.6 and Gemma 2B at 46.5. The instruction-tuned StableLM 2 Zephyr 1.6B averages 49.7, the highest of the sub-2B entries in that table, ahead of Phi-1.5 at 47.7 and behind the 2.7B Phi-2 at 61.3. The report’s abstract calls Stable LM 2 1.6B the state-of-the-art open model under 2B parameters at the time of publication. Stability built the Zephyr variant in three stages. It fine-tuned the base model on seven public instruction datasets, 826,938 samples after dropping conversations longer than eight turns, then aligned the result with direct preference optimisation, which trains on pairs of preferred and rejected answers, using UltraFeedback and Intel’s Orca Pairs and borrowing most hyperparameters from the recipe behind Hugging Face’s Zephyr 7B. A third stage taught the model who made it and what a language model cannot do, using 10,000 opening messages the base model wrote for itself. The variant scores 5.42 on MT-Bench, where a larger model judges answers to open-ended questions, above Qwen 1.5 1.8B Chat at 5.29 and Gemma 2B Instruct at 5.19 and below Stability’s own StableLM Zephyr 3B at 6.64. Chart: Bellagente et al., the authors' own figures. Stability put both sets of weights on Hugging Face along with quantised builds for llama.cpp, Apple’s MLX and Intel’s OpenVINO. The report measures 127 tok/s at 11 W for the 4-bit MLX build on an M2 Mac mini with 8 GB, against 71 tok/s at 6 W in FP16, and 99 tok/s for a 4-bit GGUF build on a 2023 MacBook Pro with 16 GB, while stating these figures are meant as a practical reference rather than rigorous benchmarking. Neither release is open source. The report says the model ships under a non-commercial licence, the Zephyr model card names the Stability AI Non-Commercial Research Community License and points commercial users to the company, and the base model card now carries the Stability AI Community License with commercial use running through a Stability membership. Source: https://arxiv.org/abs/2402.17834 Read the article: https://llmobile.kavents.com/ticker/stable-lm-2-zephyr/Stability AIResearchBenchmarksOpen weightsApple Siliconhttps://llmobile.kavents.com/ticker/mobillama/https://llmobile.kavents.com/ticker/mobillama/Mon, 26 Feb 2024 19:59:00 +0100Researchers at MBZUAI published MobiLlama on February 26, 2024, a language model of 0.5B parameters aimed at devices that cannot host a large one. The stated aim is a fully transparent small model rather than open weights alone, so the authors release the complete pre-training data pipeline, the training and evaluation code, the weights and more than 300 intermediate checkpoints. They also derive a 0.8B version from the same design. The design point is a single feed-forward block shared by every transformer layer. Each transformer block normally carries its own feed-forward network, the part that reworks each token’s representation after attention, and the authors measure those layers as 65% of all trainable parameters in their 1.2B reference model. Reusing one block across all 22 layers cuts trainable parameters by 60%, from 1.2B to 0.52B, while keeping the 22 layers and the hidden size of 2048 that the reference model has. The authors report pre-training the shared version in 7 days and 26.6K A100 GPU hours, against 12 days and 46.1K hours for the unshared 1.2B model. Diagram: Thawakar et al. For deployment the authors measured a smartphone with a Snapdragon 685, a laptop with an i7 CPU and a desktop with an RTX 2080 Ti, using 4-bit GGUF builds on the phone and the laptop. On the phone they put MobiLlama 0.5B at 770 MB of RAM and 7.02 tok/s, drawing 5.32 mAh per 1,000 tokens at 13.02% CPU utilisation. They measured Llama 2 7B on the same phone at 4287 MB, 1.19 tok/s and 10.07 mAh per 1,000 tokens with 77.41% CPU utilisation, and Phi-2 2.7B at 1893 MB and 2.88 tok/s. On the laptop CPU the 0.5B model reaches 36.32 tok/s in 799 MB. MobiLlama 0.5B averages 46.00 across nine benchmarks from the Open LLM Leaderboard in the authors’ own evaluation, which they compare with 43.57 for pythia-410m, and the 0.8B version, made by widening the shared block, averages 46.67. The models were pre-trained on 1.2T tokens from the Amber dataset of LLM360, where the two largest parts are a curated web crawl and code. Weights and chat-tuned variants are on Hugging Face under Apache 2.0, and the repository carries the training code and an Android app package for running the model on a phone. Source: https://arxiv.org/abs/2402.16840 Read the article: https://llmobile.kavents.com/ticker/mobillama/ResearchOpen weightsOpen sourceAndroidQuantisationhttps://llmobile.kavents.com/ticker/tinyllava/https://llmobile.kavents.com/ticker/tinyllava/Thu, 22 Feb 2024 06:05:30 +0100Researchers at Beihang University and Tsinghua University posted TinyLLaVA on February 22, 2024, a framework for building small vision-language models out of an existing language model and an existing image encoder. Their best configuration, which the paper calls TinyLLaVA-3.1B, scores higher than the 7B LLaVA-1.5 on seven of the nine benchmarks in the paper’s comparison table, ties it on GQA at 62.0, and trails it on MME, where the same table gives LLaVA-1.5 1510.7 against 1464.9. The authors report 79.9 against 78.5 on VQAv2 and 75.8 against 63.4 on LLaVA-Bench-in-the-Wild, and put the model ahead of the 7B Qwen-VL on four of the five benchmarks they share, behind it on TextVQA at 59.1 against 63.8. The framework treats four things as variables, the small language model, the vision encoder that turns an image into patch features, the connector that maps those features into the language model’s embedding space, and the training recipe. The authors instantiate it with TinyLlama at 1.1B, StableLM-2 at 1.6B and Phi-2 at 2.7B as language models, CLIP at 0.3B and SigLIP at 0.4B as vision encoders, and either the 558k-caption LLaVA-1.5 data or the 1246k-caption ShareGPT4V data. Their base recipe updates only the connector during pre-training, while their share recipe freezes the first 12 layers of the vision encoder and updates the rest of the model. The ablations put most of the movement in the vision encoder and the recipe. Swapping CLIP for SigLIP raises the Phi-2 variant’s TextVQA score from 51.4 to 55.6 and the TinyLlama variant’s from 45.8 to 49.1, which the authors attribute in part to SigLIP running at 384 pixels against 336 and emitting 729 visual tokens against 576. A two-layer MLP connector beat a resampler on four of six benchmarks in the single comparison the paper runs, 74.0 against 70.1 on VQAv2 and 58.1 against 54.2 on GQA, with the resampler ahead on SQA-I and MM-Vet. The authors report that the share recipe, which fine-tunes part of the vision encoder rather than freezing all of it, improved every variant pre-trained on ShareGPT4V. Model size still helps when data and recipe are held fixed, and with CLIP under the base recipe the Phi-2 variant leads the StableLM-2 and TinyLlama ones on all seven benchmarks in the appendix table. The ordering flips once the recipe changes, with the 1.5B SigLIP TinyLlama model on the share recipe beating the 1.9B CLIP StableLM-2 model on the base recipe on six of seven benchmarks. The authors add a hallucination caveat, reporting that the share recipe lowers POPE accuracy for the StableLM-2 and Phi-2 variants even as their other scores rise, and they conjecture that smaller language models need more trainable parameters during pre-training while larger ones suffer from them. The paper carries no on-device measurement. It states no latency, no memory footprint and no target hardware, and argues the small-scale case from training and inference cost rather than from phone or edge deployment. The authors wrote that weights and code would be made public, and the project repository and the 3.1B weights carry the Apache 2.0 license. Source: https://arxiv.org/abs/2402.14289 Read the article: https://llmobile.kavents.com/ticker/tinyllava/ResearchBenchmarksPhiOpen weightsOpen sourcehttps://llmobile.kavents.com/ticker/gemma/https://llmobile.kavents.com/ticker/gemma/Wed, 21 Feb 2024 18:00:00 +0100Google released Gemma on February 21, 2024, the first two models in the line, at 2B and 7B parameters and in a pretrained and an instruction-tuned checkpoint each. The technical report presents the 7B as a model for deployment on GPU and TPU and the 2B as one for CPU and on-device applications. Google says Gemma was built from the same research and technology used to create Gemini and shares technical and infrastructure components with it. Both sizes are decoder-only transformers trained on a context length of 8192 tokens. The 2B has 18 layers, a model dimension of 2048 and a single key-value head, since Google’s ablations found multi-query attention works well at small scale, while the 7B has 28 layers and keeps standard multi-head attention across 16 heads. Both inherit Gemini’s 256k-entry vocabulary, which puts 524M of the 2B’s parameters into embeddings and leaves 1.98B elsewhere. Google reports training the 2B on 3T tokens and the 7B on 6T, mostly English web documents, mathematics and code. Google reports Gemma 7B at 64.3 on MMLU 5-shot against 54.8 for Llama-2 13B, 46.4 on GSM8K against 28.7, and 32.3 on HumanEval against 18.3, for an average of 56.9 across 18 academic benchmarks. Google puts the 2B at 42.3 on MMLU and 45.0 on average, ahead of Llama-2 7B on the mathematics and coding tasks and behind it overall. Google states that Gemma outperforms similarly sized open models on 11 of the 18 text-based tasks, and notes it could not rerun the Llama-2 evaluations itself because of that model’s licensing, so it cites Meta’s published figures. Google's own figures for Gemma 7B against Llama-2. Chart: Google. The weights went up on Kaggle and Hugging Face, with the models also runnable from Colab and Vertex AI, and Google provided toolchains for inference and supervised fine-tuning across JAX, PyTorch and TensorFlow through native Keras 3.0. Hugging Face added Gemma to Transformers 4.38 on announcement day. llama.cpp merged Gemma support the same day, within half an hour of the pull request opening, which is what brought the 2B into quantised local runs on consumer hardware. Google states that the models run across laptop, desktop, IoT, mobile and cloud. The weights are open but the licence is not a standard open source one. Google publishes them under its own Gemma Terms of Use, which allow use, modification and redistribution provided that downstream recipients get the same terms and the separate Prohibited Use Policy, and which reserve Google’s right to restrict uses it considers non-compliant. Google says the terms permit responsible commercial usage and distribution for all organisations regardless of size. Alongside the models Google shipped a Responsible Generative AI Toolkit with a safety classification method, a tool for debugging model behaviour and written guidance for model builders. Source: https://blog.google/technology/developers/gemma-open-models/ Read the article: https://llmobile.kavents.com/ticker/gemma/GoogleGemmaOpen weightsllama.cppBenchmarkshttps://llmobile.kavents.com/ticker/mobilevlm-v2/https://llmobile.kavents.com/ticker/mobilevlm-v2/Tue, 06 Feb 2024 08:16:36 +0100Researchers at Meituan, Zhejiang University and Dalian University of Technology published MobileVLM V2 on February 6, 2024, a family of vision language models at 1.7B, 3B and 7B parameters, each pairing a CLIP ViT-L/14 encoder that reads images at 336 by 336 pixels with a MobileLLaMA or Vicuna language model. Averaged over six benchmarks covering visual question answering, science questions, reading text in images, object hallucination and two multimodal test suites, the authors score the 1.7B model at 64.2 against 62.7 for LLaVA-1.5 3.3B, and the 3B model at 68.1 against 66.7 for MoE-LLaVA-2.7B×4, a mixture of experts where only part of the model is active per token. Their 7B model reaches 72.1, ahead of ShareGPT4V 7B at 70.8 and LLaVA-1.5 7B at 68.8 on the same average. The authors measured generation over 256 output tokens on an NVIDIA AGX Jetson Orin developer board, not on a phone, with the language model quantised to 4-bit and run through llama.cpp. They report 51.63 tok/s for the 1.7B model, 30.80 tok/s for the 3B and 15.49 tok/s for the 7B, against 43.39, 20.45 and 12.96 tok/s for the three LLaVA-1.5 sizes in the same table. The authors state they wrote their own CUDA implementation of the new projector for that board. Chart: Chu et al., measured on an NVIDIA Jetson Orin with llama.cpp. Most of the speed comes from how much of the image reaches the language model. The encoder turns a 336 by 336 picture into 576 patch tokens, and the projector between the two models, which the authors call LDPv2, first runs two 1 by 1 convolutions to match the language model’s width. It then averages every 2 by 2 block of neighbouring patches into a single token, leaving 144, and finally adds a small depthwise convolution back onto the result so each surviving token keeps a sense of where it sat in the picture. That positional part holds 0.02M parameters against 12.64M for the equivalent block in the first MobileVLM, and the whole projector 6.32M against 18.94M, while the six-benchmark average in the authors’ ablation rises from 62.8 to 64.2. Diagram: Chu et al., MobileVLM V2. Dropping tokens costs accuracy on fine detail. The authors removed the pooling step from the 7B model, which leaves all 576 tokens and puts it at the same latency as ShareGPT4V, and the six-benchmark average rises from 72.1 to 73.5, with most of the gain on TextVQA, a reading task full of small objects. vivo AI Lab took the same 2 by 2 merge further nine months later in BlueLM-V-3B, cutting 729 tokens per image tile to 196 and measuring the result on a phone NPU. The rest of the gain over the first MobileVLM comes from data and from what stays trainable. The authors pretrain on 1.2 million captioned images from ShareGPT4V, then run a second stage over 2.4 million samples drawn from Visual Dialog, TextVQA, COCO Caption, SBU and other academic sets, 3.6 million in total, and they train the projector together with the language model in both stages while the vision encoder stays frozen, where LLaVA-1.5 and the first MobileVLM keep the language model fixed during pretraining. Both stages together took 8 NVIDIA A100 GPUs for about 14 hours. Meituan put weights for all three sizes on Hugging Face the same day, and the code is published under the Apache 2.0 license. Source: https://arxiv.org/abs/2402.03766 Read the article: https://llmobile.kavents.com/ticker/mobilevlm-v2/ResearchBenchmarksllama.cppOpen weightsNvidiahttps://llmobile.kavents.com/ticker/tinyllama/https://llmobile.kavents.com/ticker/tinyllama/Thu, 04 Jan 2024 18:54:00 +0100Researchers from the StatNLP group at Singapore University of Technology and Design published TinyLlama on January 4, 2024, a language model of 1.1B parameters pretrained on 3 trillion tokens. The authors set out to see how a small model behaves when it is trained on far more tokens than the compute-optimal scaling law of Hoffmann et al. recommends for its size, and they state that this is, to their knowledge, the first attempt to train a model of around 1B parameters on that much data. The corpus mixes natural language from SlimPajama with code from the StarCoder training data at a sampling ratio of roughly 7 to 3, which the authors put at about 950 billion tokens once the two are merged and the GitHub part of SlimPajama is dropped to avoid duplication. TinyLlama passes over that set for approximately three epochs, which is how the 3 trillion cumulative figure is reached. Pretraining ran on 16 A100-40G GPUs, and the project repository states that the run started on September 1, 2023 and was budgeted at 90 days. TinyLlama copies the architecture and tokenizer of Meta’s Llama 2, so it carries rotary position embeddings, RMSNorm pre-normalisation and SwiGLU activations across 22 layers with a hidden size of 2048 and a 2048-token context. It also inherits grouped-query attention, where the 32 query heads share four groups of key and value representations instead of each head holding its own, which the authors use to cut memory bandwidth and speed up inference. They report a training throughput of 24,000 tokens per second per A100-40G GPU, and measure 3,456 GPU hours to cover 300 billion tokens against 4,830 hours for Pythia-1.0B and 7,920 for MPT-1.3B in their own comparison. They attribute that speed to existing open-source components rather than anything new, naming Fully Sharded Data Parallel for spreading training across nodes, FlashAttention-2, fused implementations of layer normalisation, the cross entropy loss and the rotary position embedding, and the fused SwiGLU module from xFormers. Across seven zero-shot commonsense reasoning benchmarks the authors’ own table puts TinyLlama at an average of 52.99, ahead of OPT-1.3B at 51.44, Pythia-1.4B at 51.33 and Pythia-1.0B at 48.30, with the widest margin on HellaSwag at 59.20 against 53.65 for OPT-1.3B. It does not lead every column, and Pythia-1.4B stays ahead on BoolQ at 63.27 against 57.83. On the InstructEval suite TinyLlama averages 19.87 against 17.72 for Pythia-1.4B and 16.95 for OPT-1.3B, with most of that difference coming from HumanEval, where it scores 9.15 against 4.27. The authors put the device case briefly, writing in the paper that the compact architecture “can enable end-user applications on mobile devices”, and the repository describes the model as suited to applications with a restricted computation and memory footprint. It puts the 4-bit quantised weights at 637 MB and names offline real-time machine translation and drafting tokens that a larger model then checks as the uses it has in mind. Neither the paper nor the repository reports a measurement on a phone. Weights and all intermediate checkpoints are on Hugging Face under Apache 2.0, with the pre-training code in the repository. Source: https://arxiv.org/abs/2401.02385 Read the article: https://llmobile.kavents.com/ticker/tinyllama/LlamaResearchOpen weightsOpen sourcehttps://llmobile.kavents.com/ticker/phi-2/https://llmobile.kavents.com/ticker/phi-2/Tue, 12 Dec 2023 18:00:00 +0100Microsoft released Phi-2 on December 12, 2023, a base language model with 2.7 billion parameters. The company says that on complex benchmarks it matches or outperforms models up to 25 times larger, and that it surpasses Mistral and Llama-2 at 7B and 13B parameters on various aggregated benchmarks. Microsoft’s own table scores Phi-2 at 53.7 on grouped coding benchmarks against 38.3 for Llama-2-70B, and at 61.1 on math against 64.1 for the same model. Against Google’s Gemini Nano 2 at 3.2B, Microsoft reports 59.3 versus 42.4 on BigBench-Hard and 59.1 versus 27.2 on MBPP, using Google’s own reported figures for the comparison. Microsoft trained the model on 1.4 trillion tokens from multiple passes over a mixture of synthetic and web data for language and code, following what it calls textbook-quality data. The synthetic sets were written to teach common sense reasoning and general knowledge such as science, daily activities and theory of mind, and the web portion was filtered for educational value and content quality. The model card puts the underlying dataset at 250 billion tokens, combining synthetic text generated with GPT-3.5 and web data from Falcon RefinedWeb and SlimPajama that was assessed with GPT-4. Microsoft did not train Phi-2 from scratch. It started from the 1.3-billion-parameter Phi-1.5 and embedded that model’s knowledge inside the larger one, a step the company says both accelerates training convergence and raises Phi-2’s benchmark scores. Training then took 14 days on 96 A100 GPUs, and the model card lists a context length of 2048 tokens. Phi-2 is a base model that went through neither reinforcement learning from human feedback nor instruction tuning. Chart: Microsoft's own evaluation figures for the two models. Microsoft published the weights in the Azure AI Studio model catalog and on Hugging Face, where the first model card, dated December 13, 2023, named the Microsoft Research License and limited use to research. Microsoft replaced that file on January 5, 2024, and the model card has since stated “The model is licensed under the MIT license”, which the repository’s commit history dates to that day. Source: https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/ Read the article: https://llmobile.kavents.com/ticker/phi-2/MicrosoftPhiBenchmarksResearchOpen weightshttps://llmobile.kavents.com/ticker/stable-lm-zephyr/https://llmobile.kavents.com/ticker/stable-lm-zephyr/Thu, 07 Dec 2023 17:00:00 +0100Stability AI released StableLM Zephyr 3B on December 7, 2023, a chat model with 3 billion parameters that the company aims at edge devices and hardware without the compute of dedicated high-end systems. Stability calls the model 60% smaller than 7B models and reports an MT-Bench score of 6.64 and an AlpacaEval win rate of 76.00%. Both numbers are Stability’s own, measured on two evaluations the company links from the announcement. MT-Bench scores answers to open-ended questions with a larger language model acting as judge, and AlpacaEval measures how often a judge prefers a model’s reply over a reference answer. In the table Stability published with the model card, the 3B model sits above Falcon-Instruct at 40B parameters, which scored 5.17 on MT-Bench, and MPT-Chat at 7B with 5.42, and below Zephyr-7b-beta at 7.34 and Llama2-Chat at 70B with 6.86. Table: Stability AI, the company's own benchmark figures. The model extends Stability’s earlier StableLM 3B-4e1t and borrows its training pipeline from Hugging Face’s Zephyr 7B, according to Stability. Stability first ran supervised fine-tuning on the UltraChat, MetaMathQA, Evol Wizard and Capybara instruction datasets, then aligned the result with direct preference optimisation, which trains a model on pairs of preferred and rejected answers instead of on correct answers alone. That second stage used UltraFeedback from the OpenBMB research group, a set of 64,000 prompts with model responses. Stability put the weights on Hugging Face and linked an example notebook for tuning the model’s speed. The release is not open source. Stability states that it ships under a non-commercial community licence permitting non-commercial use, and tells anyone who wants the model in a commercial product to contact the company for separate terms. Source: https://stability.ai/news/stablelm-zephyr-3b-stability-llm Read the article: https://llmobile.kavents.com/ticker/stable-lm-zephyr/Stability AIOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/phi-1-5/https://llmobile.kavents.com/ticker/phi-1-5/Mon, 11 Sep 2023 16:00:00 +0200Microsoft Research published phi-1.5 on September 11, 2023, a 1.3-billion-parameter model that applies the synthetic textbook data approach of phi-1 to common sense reasoning and general world knowledge rather than Python code. The authors report common sense benchmark results comparable to models ten times the size trained on more than ten times as much data, and describe natural language performance as comparable to models five times larger. phi-1.5 scores 0.734 on WinoGrande against 0.691 for Llama2-7B, 0.444 on ARC-Challenge against 0.434, and 0.526 on SIQA against 0.480, all measured in Microsoft’s own evaluation pipeline. The gap widens on multi-step reasoning, with 40.2 on GSM8K grade-school maths against 14.6 for Llama2-7B, 34.1 on HumanEval against 12.8, and 37.7 on MBPP against 20.8, which the authors note puts its coding scores at or above Llama-65B. MMLU runs the other way, at 0.376 for phi-1.5 and 0.453 for Llama2-7B. Chart: Microsoft, Figure 1 of the technical report. The training set combines 7B tokens from phi-1 with roughly 20B tokens of newly generated textbook-like text, seeded from 20,000 selected topics spanning science, daily activities and theory of mind. That yields a 30B-token dataset whose only non-synthetic part is 6B tokens of filtered code, and training runs over 150B tokens, 80 percent of them from the new synthetic data. Microsoft puts the cost at 1,500 GPU hours on a single A100-80G, against more than 80,000 hours for Llama-7B. The same report covers two variants built to test what web data adds. phi-1.5-web-only trains purely on 95B tokens of filtered web text, 88B of it from the Falcon RefinedWeb dataset and 7B of code from The Stack and Stack Overflow, while phi-1.5-web mixes filtered web data, phi-1’s code and the synthetic text in shares of roughly 40, 20 and 40 percent. phi-1.5-web comes out ahead on reasoning at 44.6 on GSM8K, 41.4 on HumanEval and 43.5 on MBPP. The authors list hallucinations and the potential for toxic and biased generations among the traits phi-1.5 shares with larger models, and write that it “is not immune” despite the absence of web data in its training set. On an 86-prompt probe they wrote themselves, phi-1.5 passed 47 and failed 34, while Llama2-7B and Falcon-7B failed 54 and 50. Microsoft released the raw base model with no instruction fine-tuning and no RLHF, and the weights sit on Hugging Face under the MIT license. Source: https://arxiv.org/abs/2309.05463 Read the article: https://llmobile.kavents.com/ticker/phi-1-5/MicrosoftPhiResearchBenchmarksOpen weightshttps://llmobile.kavents.com/ticker/phi-1/https://llmobile.kavents.com/ticker/phi-1/Tue, 20 Jun 2023 18:14:00 +0200Microsoft Research published phi-1 on June 20, 2023, a Python code model with 1.3B parameters. The paper reports 50.6 percent pass@1 on HumanEval and 55.5 percent on MBPP, pass@1 being the share of problems the model solves with its first generated answer. The same table lists StarCoder at 15.5B parameters and 1 trillion training tokens with 33.6 percent and 52.7 percent, GPT-3.5 at 175B parameters with 47 percent, and CodeGen-Mono at 16.1B parameters and 577B tokens with 29.3 percent. The training data comes to under 7B tokens in three parts. About 6B tokens are Python files pulled from The Stack and StackOverflow by a classifier the authors trained on roughly 100,000 GPT-4 ratings of how instructive a snippet is for someone learning to code. Under 1B tokens are synthetic Python textbooks written by GPT-3.5, and about 180M tokens are synthetic exercises with solutions, used only in the finetuning step that turns phi-1-base into phi-1. Microsoft ran the training on 8 Nvidia A100 GPUs, reaching the base model in under 4 days and spending another 7 hours on the finetuning. That amounts to about 8 passes over the pretraining data and a little over 50B tokens seen, which the paper’s chart puts at 1090 GPU hours for the 1.3B run. Chart: Microsoft, Figure 2.1 of the paper. The GPU hours on the axis are the authors' own figures. The same pipeline at 350M parameters yields phi-1-small, which the authors put at 45 percent on HumanEval and use to argue that parameter count still drives which capabilities appear at all. Microsoft posted phi-1 on Hugging Face in September 2023, where the model card lists the MIT license. Source: https://arxiv.org/abs/2306.11644 Read the article: https://llmobile.kavents.com/ticker/phi-1/MicrosoftPhiResearchBenchmarksOpen weightshttps://llmobile.kavents.com/ticker/rwkv/https://llmobile.kavents.com/ticker/rwkv/Mon, 22 May 2023 15:57:41 +0200Bo Peng and 29 co-authors published RWKV on May 22, 2023, an architecture that trains in parallel like a transformer but runs as a recurrent network when it generates text. They released pretrained weights in six sizes from 169M to 14B parameters, all trained on the Pile, and describe the 14B model as by far the largest dense RNN trained to that point. A transformer keeps a cache of every token it has already processed, so both the memory it holds and the work it does for each new token grow as the context gets longer. RWKV carries a fixed-size state instead, which holds the same amount of data at token 10 and at token 10,000. The complexity table in the paper puts RWKV at O(Td) time and O(d) space for a sequence of T tokens, against O(T²d) time and O(T² + Td) space for a standard transformer, and the authors note that the arithmetic per token does not depend on context length at all. The authors measured text generation on an NVIDIA A100 with 80 GB in float32 and report cumulative time that rises in a straight line for RWKV while the transformer baselines curve upward. In their chart, RWKV-4 3B reaches about 10 seconds of cumulative GPU time after 1024 tokens, where OPT 2.7B, GPT-Neo 2.7B, BLOOM 3B and Pythia 2.8B need roughly 57 to 63 seconds. Chart: Peng et al., licensed CC BY 4.0. Cumulative time to generate 1024 tokens in float32 on an A100. On zero-shot benchmarks the paper puts RWKV close to transformers of comparable size trained on the same data. RWKV-4 at 169M scores 65.07 on PIQA where Pythia 160M scores 62.68 and GPT-Neo 125M scores 63.06, and at 14.2B it leads an interpolated GPT-level baseline on five of seven common sense tasks while trailing it on WinoGrande and ARC-Easy. The authors list the trade-off as a limitation, since funnelling everything through one fixed state limits how well the model recalls small details from very long contexts, and they report that RWKV is more sensitive to prompt wording than a transformer. Peng publishes the code as RWKV-LM under Apache 2.0, and the weights on Hugging Face carry the same license. The repository lists rwkv.cpp among community projects, a ggml-based implementation that runs 4-bit, 8-bit and float inference on CPU, while optimised iOS and Android inference sits in the README as a to-do rather than finished work. Peng states the goal in that list. RWKV is a RNN and very friendly for edge devices. Let’s make it possible to run a LLM on your phone. Source: https://arxiv.org/abs/2305.13048 Read the article: https://llmobile.kavents.com/ticker/rwkv/RWKVResearchOpen weightsMemoryOpen sourcehttps://llmobile.kavents.com/ticker/lamini-lm/https://llmobile.kavents.com/ticker/lamini-lm/Thu, 27 Apr 2023 19:58:00 +0200Researchers at MBZUAI, the University of British Columbia and Monash University published LaMini-LM on April 27, 2023, a family of 17 distilled instruction-following models ranging from 61M to 7B parameters. The paper gives resource use as the motivation, stating that earlier distilled models of 7B to 13B parameters present “challenges for deployment in resource-constrained settings” and that the authors set out to make deployment in such settings easier. The paper names no target device and reports no memory footprint, no quantisation and no generation speed, and the authors state they trained and evaluated on V100 and A100 GPUs. The models are fine-tuned on the LaMini instruction dataset, 2.58M instruction and response pairs that the team generated with gpt-3.5-turbo from prompts taken from self-instruct, P3, FLAN and Alpaca. The authors describe it as the largest instruction dataset of its kind at the time and 50 times the size of the dataset behind Alpaca. The students are 6 encoder-decoder models built on T5 and Flan-T5 and 11 decoder-only models built on GPT-2, GPT-Neo, Cerebras-GPT, GPT-J and LLaMA. The authors evaluated the family on 15 NLP benchmarks with EleutherAI’s lm-evaluation-harness and report that LaMini-Flan-T5-248M performs on par with LLaMA-7B on the benchmark average, against a baseline with about 28 times as many parameters. They warn in the repository that their LLaMA figures are not comparable to the ones the LLaMA authors published, because they re-ran the evaluation themselves. A human rating of 114 user-oriented instructions follows the same size trend and leaves a gap to gpt-3.5-turbo, and the paper reports that the models do poorly on coding, maths and tasks that need reasoning. MBZUAI publishes the weights and the instruction dataset on Hugging Face. The repository puts the code under Apache 2.0 and the models and the data under CC BY-NC 4.0, and states that both are intended for research use only. The paper was later accepted at the EACL 2024 main conference. Source: https://arxiv.org/abs/2304.14402 Read the article: https://llmobile.kavents.com/ticker/lamini-lm/ResearchDistillationOpen weightsBenchmarkshttps://llmobile.kavents.com/ticker/distilbert/https://llmobile.kavents.com/ticker/distilbert/Wed, 02 Oct 2019 19:56:00 +0200Hugging Face published DistilBERT on October 2, 2019, a compressed version of BERT trained by knowledge distillation, where a small student model learns to reproduce the output of a larger teacher. The student carries 66M parameters against BERT-base’s 110M, and Victor Sanh and his co-authors report it retaining 97% of BERT’s score on the GLUE language understanding benchmark while running 60% faster. The authors tested the model on a phone by building a question-answering app for an iPhone 7 Plus and comparing it against their own BERT-base question-answering model. Excluding the tokenisation step, they measured DistilBERT at 71% faster, and the whole model in that app weighs 207 MB, which they note could shrink further with quantisation. On a server CPU at batch size 1, the same pair takes 410 seconds against 668 for a full pass over the STS-B development set. Three losses train the student at once. A distillation loss pushes it towards the teacher’s full output distribution rather than only the correct token, a masked language modelling loss keeps BERT’s own pre-training objective in place, and a cosine embedding loss aligns the directions of student and teacher hidden state vectors. The student halves BERT’s layer count, drops the token-type embeddings and the pooler, and starts from every second layer of the teacher, an initialisation the ablation study values at 3.69 points of GLUE macro score. Training ran on eight 16 GB V100 GPUs for about 90 hours on English Wikipedia and the Toronto Book Corpus. Hugging Face released the trained weights together with the training code in its transformers library, and published the iOS demo separately as Core ML code. The paper was accepted at the fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, co-located with NeurIPS 2019. Source: https://arxiv.org/abs/1910.01108 Read the article: https://llmobile.kavents.com/ticker/distilbert/ResearchDistillationiPhoneOpen weightshttps://llmobile.kavents.com/ticker/tinybert/https://llmobile.kavents.com/ticker/tinybert/Mon, 23 Sep 2019 15:05:00 +0200Researchers at Huawei Noah’s Ark Lab and Huazhong University of Science and Technology published TinyBERT on September 23, 2019, a compressed version of BERT-base trained by knowledge distillation, where a small student model learns to reproduce the behaviour of a larger teacher. The four-layer student carries 14.5M parameters against the teacher’s 109M, and the authors report it at 7.5x smaller and 9.4x faster on inference while reaching more than 96.8% of the teacher’s score on the GLUE language understanding benchmark. A forward pass costs 1.2B FLOPs against 22.5B for BERT-base. Rather than fitting only the logits the teacher outputs, the authors define losses on the embedding layer output and, for every Transformer layer, on the hidden states and on the attention matrices those layers produce, following a finding by Kevin Clark and co-authors that BERT’s attention weights capture substantial linguistic knowledge. Dropping the attention term costs the most in their ablation, taking a four-task development average from 75.6 to 71.0, against 72.9 without the hidden states and 73.5 without the prediction layer logits. The distillation also runs twice, first as a general stage against the unfine-tuned BERT on a large text corpus, which produces a general TinyBERT that initialises the student, then as a task-specific stage against the fine-tuned BERT on a task dataset expanded by data augmentation. Removing either stage costs more still, with that same average falling to 72.5 without the general stage and 68.5 without the task-specific one. On the GLUE test set the authors report the 14.5M-parameter model at an average of 77.0 against 79.5 for BERT-base, with 82.5 on MNLI-m, 92.6 on SST-2, 87.7 on QNLI, 71.3 on QQP, 86.4 on MRPC, 80.4 on STS-B and 66.6 on RTE. The widest gap is CoLA, the linguistic acceptability task, at 44.1 against 52.8. The authors measured the speedups on a single NVIDIA K80 GPU, not on a phone. A six-layer variant at 67M parameters averages 79.4, which the authors call on par with the teacher. Huawei released the checkpoints on Hugging Face, where the general four-layer model is published as huawei-noah/TinyBERT_General_4L_312D alongside a six-layer version, with the training code in the company’s Pretrained-Language-Model repository on GitHub. The paper later appeared in Findings of EMNLP 2020. Source: https://arxiv.org/abs/1909.10351 Read the article: https://llmobile.kavents.com/ticker/tinybert/ResearchDistillationOpen weightsBenchmarks