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【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

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
训练数据
中文LLaMA VS 中文Alpaca
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-pth

python 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.sh

Base 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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Processing layers.55.feed_forward.w2.weight
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Processing layers.59.feed_forward.w3.weight
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Processing output.weight
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 6.1
  Device 1: Tesla P40, compute capability 6.1
  Device 2: Tesla P40, compute capability 6.1
  Device 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
[  22/ 543]         layers.2.attention.wq.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 0.096 0.112 0.118 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[  23/ 543]         layers.2.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
[  24/ 543]         layers.2.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 0.111 0.118 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  25/ 543]         layers.2.attention.wo.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 0.077 0.097 0.112 0.118 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[  26/ 543]       layers.2.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  27/ 543]      layers.2.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
[  28/ 543]      layers.2.feed_forward.w2.weight -    17920 x  6656, 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
[  29/ 543]      layers.2.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  30/ 543]             layers.2.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  31/ 543]         layers.3.attention.wq.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 0.096 0.111 0.117 0.112 0.097 0.077 0.056 0.039 0.025 0.021
[  32/ 543]         layers.3.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 0.112 0.119 0.112 0.097 0.076 0.056 0.038 0.025 0.021
[  33/ 543]         layers.3.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 0.111 0.118 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  34/ 543]         layers.3.attention.wo.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 0.097 0.111 0.117 0.111 0.097 0.077 0.056 0.039 0.025 0.021
[  35/ 543]       layers.3.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  36/ 543]      layers.3.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
[  37/ 543]      layers.3.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 0.056 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  38/ 543]      layers.3.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  39/ 543]             layers.3.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  40/ 543]         layers.4.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
[  41/ 543]         layers.4.attention.wk.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  42/ 543]         layers.4.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  43/ 543]         layers.4.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
[  44/ 543]       layers.4.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  45/ 543]      layers.4.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
[  46/ 543]      layers.4.feed_forward.w2.weight -    17920 x  6656, type =    f16, quantizing .. size =   227.50 MB ->    63.98 MB | hist: 0.037 0.016 0.025 0.039 0.057 0.077 0.097 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  47/ 543]      layers.4.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  48/ 543]             layers.4.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  49/ 543]         layers.5.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
[  50/ 543]         layers.5.attention.wk.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 0.097 0.111 0.117 0.111 0.097 0.077 0.056 0.039 0.025 0.021
[  51/ 543]         layers.5.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  52/ 543]         layers.5.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 0.097 0.077 0.057 0.039 0.025 0.021
[  53/ 543]       layers.5.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  54/ 543]      layers.5.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
[  55/ 543]      layers.5.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 0.097 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  56/ 543]      layers.5.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  57/ 543]             layers.5.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  58/ 543]         layers.6.attention.wq.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 0.117 0.111 0.097 0.077 0.057 0.039 0.025 0.021
[  59/ 543]         layers.6.attention.wk.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 0.111 0.117 0.111 0.097 0.077 0.057 0.039 0.025 0.021
[  60/ 543]         layers.6.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  61/ 543]         layers.6.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
[  62/ 543]       layers.6.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  63/ 543]      layers.6.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
[  64/ 543]      layers.6.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 0.097 0.111 0.117 0.111 0.097 0.077 0.057 0.039 0.025 0.021
[  65/ 543]      layers.6.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  66/ 543]             layers.6.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  67/ 543]         layers.7.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
[  68/ 543]         layers.7.attention.wk.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 0.097 0.111 0.117 0.112 0.096 0.077 0.056 0.039 0.025 0.021
[  69/ 543]         layers.7.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  70/ 543]         layers.7.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
[  71/ 543]       layers.7.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  72/ 543]      layers.7.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
[  73/ 543]      layers.7.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  74/ 543]      layers.7.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  75/ 543]             layers.7.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  76/ 543]         layers.8.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 MB | hist: 0.036 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
[  77/ 543]         layers.8.attention.wk.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 0.111 0.117 0.112 0.097 0.077 0.057 0.039 0.025 0.021
[  78/ 543]         layers.8.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 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
[  79/ 543]         layers.8.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
[  80/ 543]       layers.8.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
[  81/ 543]      layers.8.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
[  82/ 543]      layers.8.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 0.097 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  83/ 543]      layers.8.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 0.116 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  84/ 543]             layers.8.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[  85/ 543]         layers.9.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 MB | hist: 0.036 0.016 0.025 0.039 0.057 0.077 0.097 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  86/ 543]         layers.9.attention.wk.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 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
[  87/ 543]         layers.9.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
[  88/ 543]         layers.9.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
[  89/ 543]       layers.9.attention_norm.weight -             6656, type =    f32, size =    0.025 MB
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[ 112/ 543]        layers.12.attention.wq.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 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
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[ 121/ 543]        layers.13.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 MB | hist: 0.036 0.016 0.025 0.039 0.057 0.077 0.096 0.111 0.117 0.111 0.097 0.077 0.057 0.039 0.025 0.021
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[ 130/ 543]        layers.14.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
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[ 139/ 543]        layers.15.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
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[ 148/ 543]        layers.16.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
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[ 211/ 543]        layers.23.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
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[ 229/ 543]        layers.25.attention.wq.weight -     6656 x  6656, type =    f16, quantizing .. size =    84.50 MB ->    23.77 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
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[ 231/ 543]        layers.25.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 0.057 0.077 0.096 0.111 0.117 0.111 0.096 0.077 0.056 0.039 0.025 0.021
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[ 237/ 543]            layers.25.ffn_norm.weight -             6656, type =    f32, size =    0.025 MB
[ 238/ 543]        layers.26.attention.wq.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 0.111 0.117 0.111 0.096 0.077 0.057 0.039 0.025 0.021
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[ 247/ 543]        layers.27.attention.wq.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 0.096 0.112 0.118 0.112 0.097 0.077 0.056 0.039 0.025 0.021
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[ 256/ 543]        layers.28.attention.wq.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 0.111 0.117 0.111 0.097 0.077 0.056 0.039 0.025 0.021
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[ 265/ 543]        layers.29.attention.wq.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 0.117 0.112 0.096 0.077 0.056 0.039 0.025 0.021
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[ 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 0.021

main: 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

【AI实战】快速搭建中文 Alpaca 33B 大模型 Chinese-Alpaca-33B