# llama.cpp 1. 转换语言模型 生成 gguf python convert_hf_to_gguf.py ./model_path --outtype f32 2. 量化语言模型: ./llama-quantize ./model_path/Qwen2-VL-2B-Instruct-F32.gguf Qwen2-VL-2B-Instruct-Q4_K_M.gguf Q4_K_M 3. 转换视觉模型 python examples/llava/qwen2_vl_surgery.py ./model_path 4. 推理 llama-qwen2vl-cli -m Qwen2-VL-2B-Instruct-Q4_K_M.gguf --mmproj qwen2-vl-2b-instruct-vision.gguf -p "描述这图片" --image "1.png" ######## llama-quantize usage: ./llama-quantize [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads] --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing --pure: Disable k-quant mixtures and quantize all tensors to the same type --imatrix file_name: use data in file_name as importance matrix for quant optimizations --include-weights tensor_name: use importance matrix for this/these tensor(s) --exclude-weights tensor_name: use importance matrix for this/these tensor(s) --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor --keep-split: will generate quantized model in the same shards as input --override-kv KEY=TYPE:VALUE Advanced option to override model metadata by key in the quantized model. May be specified multiple times. Note: --include-weights and --exclude-weights cannot be used together Allowed quantization types: 2 or Q4_0 : 4.34G, +0.4685 ppl @ Llama-3-8B 3 or Q4_1 : 4.78G, +0.4511 ppl @ Llama-3-8B 8 or Q5_0 : 5.21G, +0.1316 ppl @ Llama-3-8B 9 or Q5_1 : 5.65G, +0.1062 ppl @ Llama-3-8B 19 or IQ2_XXS : 2.06 bpw quantization 20 or IQ2_XS : 2.31 bpw quantization 28 or IQ2_S : 2.5 bpw quantization 29 or IQ2_M : 2.7 bpw quantization 24 or IQ1_S : 1.56 bpw quantization 31 or IQ1_M : 1.75 bpw quantization 36 or TQ1_0 : 1.69 bpw ternarization 37 or TQ2_0 : 2.06 bpw ternarization 10 or Q2_K : 2.96G, +3.5199 ppl @ Llama-3-8B 21 or Q2_K_S : 2.96G, +3.1836 ppl @ Llama-3-8B 23 or IQ3_XXS : 3.06 bpw quantization 26 or IQ3_S : 3.44 bpw quantization 27 or IQ3_M : 3.66 bpw quantization mix 12 or Q3_K : alias for Q3_K_M 22 or IQ3_XS : 3.3 bpw quantization 11 or Q3_K_S : 3.41G, +1.6321 ppl @ Llama-3-8B 12 or Q3_K_M : 3.74G, +0.6569 ppl @ Llama-3-8B 13 or Q3_K_L : 4.03G, +0.5562 ppl @ Llama-3-8B 25 or IQ4_NL : 4.50 bpw non-linear quantization 30 or IQ4_XS : 4.25 bpw non-linear quantization 15 or Q4_K : alias for Q4_K_M 14 or Q4_K_S : 4.37G, +0.2689 ppl @ Llama-3-8B 15 or Q4_K_M : 4.58G, +0.1754 ppl @ Llama-3-8B 17 or Q5_K : alias for Q5_K_M 16 or Q5_K_S : 5.21G, +0.1049 ppl @ Llama-3-8B 17 or Q5_K_M : 5.33G, +0.0569 ppl @ Llama-3-8B 18 or Q6_K : 6.14G, +0.0217 ppl @ Llama-3-8B 7 or Q8_0 : 7.96G, +0.0026 ppl @ Llama-3-8B 1 or F16 : 14.00G, +0.0020 ppl @ Mistral-7B 32 or BF16 : 14.00G, -0.0050 ppl @ Mistral-7B 0 or F32 : 26.00G @ 7B COPY : only copy tensors, no quantizing