AI正在发生 未来正在更新LFM2.5-VL-3B-Uncensored-GGUF
LFM2.5-VL-3B-Uncensored-GGUF English | 📖 中文文档 Uncensored vision-language edge model · abliterix Trial 65 merged into official LFM2.5-VL-3B · imatrix-calibrated GGUFs + mmproj LFM2.5-VL-3B is Liquid AI's official 3.1B vision-language edge model : the LFM2.5-2.6B hybrid language backbone (30 layers: 22 double-gated short-convolution blocks + 8 GQA, 128K context, 128K vocab) paired with a SigLIP2 NaFlex vision encoder (256×256, patch 16) and a language-aligned projector. This release merges the abliterix Trial 65 LoRA (from our LFM2.5-2.6B-Uncensored , rank-1, alpha = r = 1) into the language-model layers of the official VL checkpoint ( q/k/v/out_proj , feed_forward.w2 , conv.out_proj — 84 tensors across 30 layers). The vision encoder and projector are untouched , so multimodal capability is fully preserved. Build steps: LoRA merge into the VL checkpoint: W += (B @ A) * (alpha / r) , applied to model.language_model.layers.* . BF16 GGUF conversion of the merged model (llama.cpp lfm2 architecture) + mmproj export of the vision tower via convert_hf_to_gguf.py --mmproj --outtype bf16 . imatrix calibration — reused the 401-chunk (≈1.6M tokens) importance matrix from the 2.6B release (same architecture, weights closely related). Quantization with llama-quantize --imatrix into five tiers.
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LFM2.5-VL-3B-Uncensored-GGUF English | 📖 中文文档 Uncensored vision-language edge model · abliterix Trial 65 merged into official LFM2.5-VL-3B · imatrix-calibrated GGUFs + mmproj LFM2.5-VL-3B is Liquid AI's official 3.1B vision-language edge model : the LFM2.5-2.6B hybrid language backbone (30 layers: 22 double-gated short-convolution blocks + 8 GQA, 128K context, 128K vocab) paired with a SigLIP2 NaFlex vision encoder (256×256, patch 16) and a language-aligned projector. This release merges the abliterix Trial 65 LoRA (from our LFM2.5-2.6B-Uncensored , rank-1, alpha = r = 1) into the language-model layers of the official VL checkpoint ( q/k/v/out_proj , feed_forward.w2 , conv.out_proj — 84 tensors across 30 layers). The vision encoder and projector are untouched , so multimodal capability is fully preserved. Build steps: LoRA merge into the VL checkpoint: W += (B @ A) * (alpha / r) , applied to model.language_model.layers.* . BF16 GGUF conversion of the merged model (llama.cpp lfm2 architecture) + mmproj export of the vision tower via convert_hf_to_gguf.py --mmproj --outtype bf16 . imatrix calibration — reused the 401-chunk (≈1.6M tokens) importance matrix from the 2.6B release (same architecture, weights closely related). Quantization with llama-quantize --imatrix into five tiers.