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repliedto savoji's post 5 days ago
LFM3-1.7B from @LiquidAI is the #1 trending model on the Hub (855 likes). It takes about 1 GB at 4-bit, and in the vendor's own table it beats Qwen3.5 9B on several agent benchmarks. The edge decision moves from "are small models good enough?" to "which architecture and runtime do I pick?" One 1.7B backbone now ships as text, VL-2B and Omni-2B checkpoints with open weights. 🔍 Architecture and speed • 24 layers: 13 gated convolutions (kernel 7), 5 sliding-window attention (256 tokens), 6 global attention, chosen by a search over 14 device and backend targets • llama.cpp Q4_0 at 32K context: 1.8 to 2.8x faster decode than the next model on 3 devices • Full 128K context needs 1.6 GB of KV cache vs 4.3 GB (Qwen3.5-4B) and 5.6 GB (MiniCPM5-2B) 📊 Liquid AI's table • Average 49.37 vs 47.24 for Qwen3.5 9B; BFCL V4 71.39 vs 62.83; WebArena-Verified 38.42 vs 28.94 • Qwen3.5 9B still wins SWE-bench Verified (51.20 vs 43.40) and GPQA Diamond (80.40 vs 63.37) 🛠️ Try it • GGUF (174,177 downloads last month) runs in llama.cpp b11960+, Ollama and LM Studio; MLX 4-bit for Apple Silicon; SGLang for GPUs • llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --temp 0.1 --top-k 50 --repeat-penalty 1.1 • Omni-2B runs in the browser in the WebGPU Space ⚠️ Caveats • The numbers are vendor-reported, with no independent reproduction in the sources. The card says it is not for agentic coding or complex multi-step reasoning. https://huggingface.co/LiquidAI/LFM3-1.7B https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF https://huggingface.co/LiquidAI/LFM3-Omni-2B https://huggingface.co/spaces/LiquidAI/LFM3-Omni-2B-WebGPU Has a sub-2B model held up on your own tool-calling harness? • Medium: https://medium.com/p/05507e4e81c8 • Substack: https://sajjadpakdamansavoji.substack.com/p/liquid-ais-lfm3-turns-the-small-model • X: https://x.com/saavoji/status/2117307514177941672 • Website: https://sajjadpsavoji.github.io/blog#liquid-ais-lfm3
repliedto savoji's post 6 days ago
LFM3-1.7B from @LiquidAI is the #1 trending model on the Hub (855 likes). It takes about 1 GB at 4-bit, and in the vendor's own table it beats Qwen3.5 9B on several agent benchmarks. The edge decision moves from "are small models good enough?" to "which architecture and runtime do I pick?" One 1.7B backbone now ships as text, VL-2B and Omni-2B checkpoints with open weights. 🔍 Architecture and speed • 24 layers: 13 gated convolutions (kernel 7), 5 sliding-window attention (256 tokens), 6 global attention, chosen by a search over 14 device and backend targets • llama.cpp Q4_0 at 32K context: 1.8 to 2.8x faster decode than the next model on 3 devices • Full 128K context needs 1.6 GB of KV cache vs 4.3 GB (Qwen3.5-4B) and 5.6 GB (MiniCPM5-2B) 📊 Liquid AI's table • Average 49.37 vs 47.24 for Qwen3.5 9B; BFCL V4 71.39 vs 62.83; WebArena-Verified 38.42 vs 28.94 • Qwen3.5 9B still wins SWE-bench Verified (51.20 vs 43.40) and GPQA Diamond (80.40 vs 63.37) 🛠️ Try it • GGUF (174,177 downloads last month) runs in llama.cpp b11960+, Ollama and LM Studio; MLX 4-bit for Apple Silicon; SGLang for GPUs • llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --temp 0.1 --top-k 50 --repeat-penalty 1.1 • Omni-2B runs in the browser in the WebGPU Space ⚠️ Caveats • The numbers are vendor-reported, with no independent reproduction in the sources. The card says it is not for agentic coding or complex multi-step reasoning. https://huggingface.co/LiquidAI/LFM3-1.7B https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF https://huggingface.co/LiquidAI/LFM3-Omni-2B https://huggingface.co/spaces/LiquidAI/LFM3-Omni-2B-WebGPU Has a sub-2B model held up on your own tool-calling harness? • Medium: https://medium.com/p/05507e4e81c8 • Substack: https://sajjadpakdamansavoji.substack.com/p/liquid-ais-lfm3-turns-the-small-model • X: https://x.com/saavoji/status/2117307514177941672 • Website: https://sajjadpsavoji.github.io/blog#liquid-ais-lfm3
liked a model 26 days ago
openbmb/MiniCPM-V-4.7-1B
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