代码拉取完成，页面将自动刷新
全部开源，完全可商用的中文版 Llama2 模型及中英文 SFT 数据集，输入格式严格遵循 llama-2-chat 格式，兼容适配所有针对原版 llama-2-chat 模型的优化。
Talk is cheap, Show you the Demo.
模型下载
4bit量化
GGML Q4 模型：
我们使用了中英文 SFT 数据集，数据量 1000 万。
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
model_path = "LinkSoul/Chinese-Llama-2-7b"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path).half().cuda()
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
instruction = """[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{} [/INST]"""
prompt = instruction.format("用中文回答，When is the best time to visit Beijing, and do you have any suggestions for me?")
generate_ids = model.generate(tokenizer(prompt, return_tensors='pt').input_ids.cuda(), max_new_tokens=4096, streamer=streamer)
你可以使用仓库中的 Dockerfile，来快速制作基于 Nvidia 最新版本的 nvcr.io/nvidia/pytorch:23.06-py3 基础镜像，在任何地方使用容器来运行中文的 LLaMA2 模型应用。
docker build -t linksoul/chinese-llama2-chat .
镜像构建完毕，使用命令运行镜像即可：
docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --rm -it -v `pwd`/LinkSoul:/app/LinkSoul -p 7860:7860 linksoul/chinese-llama2-chat
想要在 CPU 环境运行 LLaMA2 模型么？使用下面的方法吧。
ggml/convert_to_ggml.py 进行转换操作，详见脚本支持的 CLI 参数。docker pull soulteary/llama2:converter 下载模型格式转换工具镜像，在 Docker 容器中使用下面的两条命令完成操作（教程 构建能够使用 CPU 运行的 MetaAI LLaMA2 中文大模型
）：python3 convert.py /app/LinkSoul/Chinese-Llama-2-7b/ --outfile /app/LinkSoul/Chinese-Llama-2-7b-ggml.bin
./quantize /app/LinkSoul/Chinese-Llama-2-7b-ggml.bin /app/LinkSoul/Chinese-Llama-2-7b-ggml-q4.bin q4_0
量化配置的定义:
转自: https://www.reddit.com/r/LocalLLaMA/comments/139yt87/notable_differences_between_q4_2_and_q5_1/
q4_0 = 32 numbers in chunk, 4 bits per weight, 1 scale value at 32-bit float (5 bits per value in average), each weight is given by the common scale * quantized value.
q4_1 = 32 numbers in chunk, 4 bits per weight, 1 scale value and 1 bias value at 32-bit float (6 bits per value in average), each weight is given by the common scale * quantized value + common bias.
q4_2 = same as q4_0, but 16 numbers in chunk, 4 bits per weight, 1 scale value that is 16-bit float, same size as q4_0 but better because chunks are smaller.
q4_3 = already dead, but analogous: q4_1 but 16 numbers in chunk, 4 bits per weight, scale value that is 16 bit and bias also 16 bits, same size as q4_1 but better because chunks are smaller.
q5_0 = 32 numbers in chunk, 5 bits per weight, 1 scale value at 16-bit float, size is 5.5 bits per weight
q5_1 = 32 numbers in a chunk, 5 bits per weight, 1 scale value at 16 bit float and 1 bias value at 16 bit, size is 6 bits per weight.
q8_0 = same as q4_0, except 8 bits per weight, 1 scale value at 32 bits, making total of 9 bits per weight.
首先需要安装额外的依赖 pip install fastapi uvicorn，然后运行仓库中的 api.py：
python api.py
默认部署在本地的 8000 端口，通过 POST 方法进行调用
curl -X POST "http://127.0.0.1:8000" \
-H 'Content-Type: application/json' \
-d '{"prompt": "你好", "history": []}'
得到的返回值为
{
"response":" 你好！我是一个人工智能语言模型，可以回答你的问题和进行对话。请问你有什么需要帮助的吗？ ",
"history":[["<<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\n If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n你好"," 你好！我是一个人工智能语言模型，可以回答你的问题和进行对话。请问你有什么需要帮助的吗？ "]],
"status":200,
"time":"2023-08-01 09:22:16"
}
DATASET="LinkSoul/instruction_merge_set"
DATA_CACHE_PATH="hf_datasets_cache"
MODEL_PATH="/PATH/TO/TRANSFORMERS/VERSION/LLAMA2"
output_dir="./checkpoints_llama2"
torchrun --nnodes=1 --node_rank=0 --nproc_per_node=8 \
--master_port=25003 \
train.py \
--model_name_or_path ${MODEL_PATH} \
--data_path ${DATASET} \
--data_cache_path ${DATA_CACHE_PATH} \
--bf16 True \
--output_dir ${output_dir} \
--num_train_epochs 1 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 1 \
--evaluation_strategy 'no' \
--save_strategy 'steps' \
--save_steps 1200 \
--save_total_limit 5 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--fsdp 'full_shard auto_wrap' \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 4096 \
--gradient_checkpointing True
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。