【AI实战】快速搭建中文 Alpaca 33B 大模型 Chinese-Alpaca-33B
- 中文 33B 大模型 Chinese-Alpaca-33B
- 环境配置
- 搭建过程
- 1.拉取 chinese-alpaca-lora-33b
- 2.合并lora权重
- 3.llaa.cpp量化
- 模型准备
- 模型权重转换为ggml的FP16格式
- 对FP16模型进行4-bit量化
- 测试
- 参考
中文 33B 大模型 Chinese-Alpaca-33B
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介绍
Chinese-Alpaca-33B 大模型在原版 LLaMA-33B 的基础上扩充了中文词表并使用了中文数据进行二次预训练，进一步提升了中文基础语义理解能力。同时，中文Alpaca模型进一步使用了中文指令数据进行精调，显著提升了模型对指令的理解和执行能力。
官网：https://github.com/ymcui/Chinese-LLaMA-Alpaca
LLaMA模型禁止商用
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训练数据
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中文LLaMA VS 中文Alpaca
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测试截图
从测试结果来看，还可以吧，但是距离chatGPT有距离啊！
环境配置
环境配置过程详情参考我的这篇文章；
【AI实战】从零开始搭建中文 LLaMA-33B 语言模型 Chinese-LLaMA-Alpaca-33B
llama-33B 模型下载、合并方法也是参考这篇文章：
【AI实战】从零开始搭建中文 LLaMA-33B 语言模型 Chinese-LLaMA-Alpaca-33B
得到的模型保存路径：“./Chinese-LLaMA-33B”
llama.cpp 量化部署 llama-33B参考这篇文章：
【AI实战】llama.cpp 量化部署 llama-33B
搭建过程
首先按照上面的步骤已经获得：
./llama-30b-hf – llama-30b 原始模型
llama.cpp 已经编译好
1.拉取 chinese-alpaca-lora-33b
执行：
cd /notebooks
git clone https://huggingface.co/ziqingyang/chinese-alpaca-lora-33b
【】可能拉取失败，耐心尝试多次就会成功！！！
比如错误信息：
Cloning into 'chinese-alpaca-lora-33b'...
fatal: unable to access 'https://huggingface.co/ziqingyang/chinese-alpaca-lora-33b/': gnutls_handshake() failed: Error in the pull function.
也可能拉取到的文件太小：
# git clone https://huggingface.co/ziqingyang/chinese-alpaca-lora-33b
Cloning into 'chinese-alpaca-lora-33b'...
remote: Enumerating objects: 19, done.
remote: Total 19 (delta 0), reused 0 (delta 0), pack-reused 19
Unpacking objects: 100% (19/19), 2.58 KiB | 440.00 KiB/s, done.# du -sh chinese-alpaca-lora-33b/
344K chinese-alpaca-lora-33b/
文件夹 chinese-alpaca-lora-33b/ 大小才 344K
执行：
rm -rf chinese-alpaca-lora-33b/
再拉取，执行：
git clone https://huggingface.co/ziqingyang/chinese-alpaca-lora-33b
正确拉取到的文件大小：
# du -sh chinese-llama-lora-33b/
2.8G chinese-llama-lora-33b/
2.合并lora权重
合并脚本：
merge_chinese-alpaca-33b.sh
cd /notebooks/Chinese-LLaMA-Alpaca
mkdir ./chinese-alpaca-33b-pthpython scripts/merge_llama_with_chinese_lora.py \--base_model ../llama-30b-hf/ \--lora_model ../chinese-alpaca-lora-33b/ \--output_type pth \--output_dir ./chinese-alpaca-33b-pth
执行合并：
sh merge_chinese-alpaca-33b.sh
输出结果到路径：./chinese-alpaca-33b-pth
输出信息：
# sh merge_chinese-alpaca-33b.shBase model: ../llama-30b-hf/
LoRA model(s) ['../chinese-alpaca-lora-33b/']:
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████| 61/61 [01:32<00:00, 1.51s/it]
Peft version: 0.3.0
Loading LoRA for 33B model
Loading LoRA ../chinese-alpaca-lora-33b/...
base_model vocab size: 32000
tokenizer vocab size: 49954
Extended vocabulary size to 49954
Loading LoRA weights
Merging with merge_and_unload...
Saving to pth format...
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Saving shard 1 of 4 into ./chinese-alpaca-33b-pth/consolidated.00.pth
Saving shard 2 of 4 into ./chinese-alpaca-33b-pth/consolidated.01.pth
Saving shard 3 of 4 into ./chinese-alpaca-33b-pth/consolidated.02.pth
Saving shard 4 of 4 into ./chinese-alpaca-33b-pth/consolidated.03.pth
Saving params.json into ./chinese-alpaca-33b-pth/params.json
3.llaa.cpp量化
模型准备
cd /notebooks/llama.cpp
mkdir zh-models/
cp /notebooks/Chinese-LLaMA-Alpaca/chinese-alpaca-33b-pth/tokenizer.model zh-models
mkdir zh-models/33B
cp /notebooks/Chinese-LLaMA-Alpaca/chinese-alpaca-33b-pth/consolidated.0* zh-models/33B/
cp /notebooks/Chinese-LLaMA-Alpaca/Chinese-alpaca-33B-pth/params.json zh-models/33B/
模型权重转换为ggml的FP16格式
执行：
python convert.py zh-models/33B/
输出信息：
# python convert.py zh-models/33B/
Loading model file zh-models/33B/consolidated.00.pth
Loading model file zh-models/33B/consolidated.01.pth
Loading model file zh-models/33B/consolidated.02.pth
Loading model file zh-models/33B/consolidated.03.pth
Loading vocab file zh-models/tokenizer.model
params: n_vocab:49954 n_embd:6656 n_mult:256 n_head:52 n_layer:60
Writing vocab...
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Wrote zh-models/33B/ggml-model-f16.bin
对FP16模型进行4-bit量化
执行：
./quantize ./zh-models/33B/ggml-model-f16.bin ./zh-models/33B/ggml-model-q4_0.bin q4_0
输出信息：
ggml_init_cublas: found 4 CUDA devices:Device 0: Tesla P40, compute capability 6.1Device 1: Tesla P40, compute capability 6.1Device 2: Tesla P40, compute capability 6.1Device 3: Tesla P40, compute capability 6.1
main: build = 796 (31cfbb1)
main: quantizing './zh-models/33B/ggml-model-f16.bin' to './zh-models/33B/ggml-model-q4_0.bin' as Q4_0
llama.cpp: loading model from ./zh-models/33B/ggml-model-f16.bin
llama.cpp: saving model to ./zh-models/33B/ggml-model-q4_0.bin
[ 1/ 543] tok_embeddings.weight - 6656 x 49954, type = f16, quantizing .. size = 634.18 MB -> 178.36 MB | hist: 0.036 0.015 0.025 0.039 0.056 0.077 0.097 0.112 0.117 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[ 2/ 543] norm.weight - 6656, type = f32, size = 0.025 MB
[ 3/ 543] output.weight - 6656 x 49954, type = f16, quantizing .. size = 634.18 MB -> 178.36 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.112 0.118 0.112 0.097 0.076 0.056 0.038 0.025 0.020
[ 4/ 543] layers.0.attention.wq.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.037 0.018 0.028 0.043 0.060 0.077 0.094 0.106 0.111 0.106 0.094 0.077 0.059 0.042 0.028 0.022
[ 5/ 543] layers.0.attention.wk.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.013 0.021 0.033 0.050 0.072 0.099 0.124 0.136 0.124 0.099 0.072 0.050 0.033 0.021 0.017
[ 6/ 543] layers.0.attention.wv.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.055 0.076 0.097 0.113 0.120 0.113 0.097 0.076 0.055 0.038 0.025 0.020
[ 7/ 543] layers.0.attention.wo.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.035 0.012 0.020 0.032 0.049 0.073 0.100 0.126 0.137 0.126 0.100 0.073 0.049 0.032 0.020 0.016
[ 8/ 543] layers.0.attention_norm.weight - 6656, type = f32, size = 0.025 MB
[ 9/ 543] layers.0.feed_forward.w1.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.077 0.097 0.113 0.118 0.113 0.097 0.077 0.056 0.038 0.025 0.020
[ 10/ 543] layers.0.feed_forward.w2.weight - 17920 x 6656, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.025 0.039 0.056 0.077 0.097 0.112 0.117 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[ 11/ 543] layers.0.feed_forward.w3.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.113 0.119 0.113 0.097 0.076 0.056 0.038 0.025 0.020
[ 12/ 543] layers.0.ffn_norm.weight - 6656, type = f32, size = 0.025 MB
[ 13/ 543] layers.1.attention.wq.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.113 0.120 0.113 0.097 0.076 0.056 0.038 0.025 0.020
[ 14/ 543] layers.1.attention.wk.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.113 0.120 0.113 0.097 0.076 0.056 0.038 0.025 0.020
[ 15/ 543] layers.1.attention.wv.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.112 0.118 0.112 0.097 0.077 0.056 0.038 0.025 0.021
[ 16/ 543] layers.1.attention.wo.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.014 0.023 0.036 0.054 0.075 0.098 0.117 0.124 0.117 0.098 0.075 0.054 0.036 0.023 0.019
[ 17/ 543] layers.1.attention_norm.weight - 6656, type = f32, size = 0.025 MB
[ 18/ 543] layers.1.feed_forward.w1.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 19/ 543] layers.1.feed_forward.w2.weight - 17920 x 6656, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 20/ 543] layers.1.feed_forward.w3.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 21/ 543] layers.1.ffn_norm.weight - 6656, type = f32, size = 0.025 MB
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[ 519/ 543] layers.57.attention.wv.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.037 0.016 0.025 0.039 0.056 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 520/ 543] layers.57.attention.wo.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.026 0.021
[ 521/ 543] layers.57.attention_norm.weight - 6656, type = f32, size = 0.025 MB
[ 522/ 543] layers.57.feed_forward.w1.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 523/ 543] layers.57.feed_forward.w2.weight - 17920 x 6656, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.025 0.039 0.056 0.077 0.097 0.112 0.117 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[ 524/ 543] layers.57.feed_forward.w3.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 525/ 543] layers.57.ffn_norm.weight - 6656, type = f32, size = 0.025 MB
[ 526/ 543] layers.58.attention.wq.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.113 0.120 0.113 0.097 0.076 0.055 0.038 0.024 0.020
[ 527/ 543] layers.58.attention.wk.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.024 0.038 0.055 0.076 0.097 0.114 0.121 0.114 0.097 0.076 0.055 0.038 0.024 0.020
[ 528/ 543] layers.58.attention.wv.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.016 0.025 0.039 0.056 0.077 0.096 0.112 0.118 0.112 0.096 0.077 0.056 0.039 0.025 0.021
[ 529/ 543] layers.58.attention.wo.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 530/ 543] layers.58.attention_norm.weight - 6656, type = f32, size = 0.025 MB
[ 531/ 543] layers.58.feed_forward.w1.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 532/ 543] layers.58.feed_forward.w2.weight - 17920 x 6656, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.077 0.097 0.112 0.118 0.112 0.097 0.077 0.056 0.038 0.025 0.021
[ 533/ 543] layers.58.feed_forward.w3.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[ 534/ 543] layers.58.ffn_norm.weight - 6656, type = f32, size = 0.025 MB
[ 535/ 543] layers.59.attention.wq.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.055 0.076 0.097 0.113 0.121 0.113 0.097 0.076 0.055 0.038 0.024 0.020
[ 536/ 543] layers.59.attention.wk.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.038 0.056 0.076 0.097 0.113 0.119 0.113 0.097 0.076 0.056 0.038 0.025 0.020
[ 537/ 543] layers.59.attention.wv.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.036 0.015 0.025 0.039 0.056 0.077 0.097 0.112 0.118 0.112 0.096 0.077 0.056 0.039 0.025 0.021
[ 538/ 543] layers.59.attention.wo.weight - 6656 x 6656, type = f16, quantizing .. size = 84.50 MB -> 23.77 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.116 0.111 0.096 0.077 0.057 0.039 0.026 0.021
[ 539/ 543] layers.59.attention_norm.weight - 6656, type = f32, size = 0.025 MB
[ 540/ 543] layers.59.feed_forward.w1.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.016 0.025 0.039 0.056 0.077 0.097 0.112 0.117 0.111 0.097 0.077 0.056 0.039 0.025 0.021
[ 541/ 543] layers.59.feed_forward.w2.weight - 17920 x 6656, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.015 0.024 0.037 0.055 0.076 0.097 0.114 0.121 0.114 0.097 0.076 0.055 0.037 0.024 0.020
[ 542/ 543] layers.59.feed_forward.w3.weight - 6656 x 17920, type = f16, quantizing .. size = 227.50 MB -> 63.98 MB | hist: 0.036 0.016 0.025 0.039 0.056 0.077 0.097 0.111 0.117 0.111 0.097 0.077 0.056 0.039 0.025 0.021
[ 543/ 543] layers.59.ffn_norm.weight - 6656, type = f32, size = 0.025 MB
llama_model_quantize_internal: model size = 62501.44 MB
llama_model_quantize_internal: quant size = 17580.74 MB
llama_model_quantize_internal: hist: 0.036 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021main: quantize time = 132816.29 ms
main: total time = 132816.29 ms
测试
执行：
./main -m zh-models/33B/ggml-model-q4_0.bin --color -f prompts/alpaca.txt -ins -c 2048 --temp 0.2 -n 256 --repeat_penalty 1.1
测试：
参考
1.【AI实战】从零开始搭建中文 LLaMA-33B 语言模型 Chinese-LLaMA-Alpaca-33B
2.https://github.com/ymcui/Chinese-LLaMA-Alpaca
3.【AI实战】llama.cpp 量化部署 llama-33B
