llama.cpp制作GGUF文件及使用 - ToB企服应用市场:ToB评测及商务社交产业平台

#代码准备
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
#编译
make
# 获取官方模型权重并将其放入./models中
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [可选] 对于使用 BPE 分词器的模型
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [可选] 适用于 Mistral-7B 等 PyTorch.bin 模型
ls ./models
<folder containing weights and tokenizer json>
# 安装Python依赖项
python3 -m pip install -r requirements.txt
# 将模型转换为ggml FP16格式
python3 convert.py models/mymodel/
# [可选] 对于使用 BPE 分词器的模型
python convert.py models/mymodel/ --vocab-type bpe
# 将模型量化为 4 位（使用 Q4_K_M 方法）
./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
# 如果现在不支持旧版本，请将 gguf 文件类型更新为当前版本
./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY

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# 开始推理 gguf 模型
./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -p "你好" -n 128

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# 交互式使用模型
./main -m models/openbuddy-llama3-8b-v21.1-8k/openbuddy-llama3-8b-Q4_K_M.gguf -n 999 -cml

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# 启动兼容openai api 的HTTP server
./server -m models/openbuddy-llama3-8b-v21.1-8k/openbuddy-llama3-8b-Q4_K_M.gguf -c 4096 --host 0.0.0.0 --port 7861

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(llamacpp) root@9pp562fqj4j6n-0:/1_11_test/hhh/llama.cpp# ./quantize
usage: ./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 quatized 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 : 3.56G, +0.2166 ppl @ LLaMA-v1-7B
3 or Q4_1 : 3.90G, +0.1585 ppl @ LLaMA-v1-7B
8 or Q5_0 : 4.33G, +0.0683 ppl @ LLaMA-v1-7B
9 or Q5_1 : 4.70G, +0.0349 ppl @ LLaMA-v1-7B
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
10 or Q2_K : 2.63G, +0.6717 ppl @ LLaMA-v1-7B
21 or Q2_K_S : 2.16G, +9.0634 ppl @ LLaMA-v1-7B
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 : 2.75G, +0.5551 ppl @ LLaMA-v1-7B
12 or Q3_K_M : 3.07G, +0.2496 ppl @ LLaMA-v1-7B
13 or Q3_K_L : 3.35G, +0.1764 ppl @ LLaMA-v1-7B
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 : 3.59G, +0.0992 ppl @ LLaMA-v1-7B
15 or Q4_K_M : 3.80G, +0.0532 ppl @ LLaMA-v1-7B
17 or Q5_K : alias for Q5_K_M
16 or Q5_K_S : 4.33G, +0.0400 ppl @ LLaMA-v1-7B
17 or Q5_K_M : 4.45G, +0.0122 ppl @ LLaMA-v1-7B
18 or Q6_K : 5.15G, +0.0008 ppl @ LLaMA-v1-7B
7 or Q8_0 : 6.70G, +0.0004 ppl @ LLaMA-v1-7B
1 or F16 : 13.00G @ 7B
0 or F32 : 26.00G @ 7B
COPY : only copy tensors, no quantizing

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