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metadata
license: openrail
datasets:
  - LinkSoul/instruction_merge_set
language:
  - zh
  - en
widget:
  - text: >-
      [INST] <<SYS>>

      You 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.
      <</SYS>>


      用中文回答，When is the best time to visit Beijing, and do you have any
      suggestions for me? [/INST]
    example_title: 北京
  - text: >-
      [INST] <<SYS>>

      You 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.
      <</SYS>>


      用英文回答，特朗普是谁？ [/INST]
    example_title: 特朗普是谁

Chinese Llama 2 7B 4bit

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。

Chinese LLaMA2 7B

基础演示

Base Demo

在线试玩

Talk is cheap, Show you the Demo.

资源下载

我们使用了中英文 SFT 数据集，数据量 1000 万。

快速测试 

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

# Original version
# model_path = "LinkSoul/Chinese-Llama-2-7b"
# 4 bit version
model_path = "LinkSoul/Chinese-Llama-2-7b-4bit"


tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if model_path.endswith("4bit"):
    model = AutoModelForCausalLM.from_pretrained(
            model_path,
            load_in_4bit=True,
            local_files_only=True,
            torch_dtype=torch.float16
        )
else:
    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("用英文回答，什么是夫妻肺片？")
generate_ids = model.generate(tokenizer(prompt, return_tensors='pt').input_ids.cuda(), max_new_tokens=4096, streamer=streamer)

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