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--- |
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license: openrail |
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pipeline_tag: text-generation |
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library_name: transformers |
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language: |
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- zh |
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- en |
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--- |
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## Original model card |
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Buy me a coffee if you like this project ;) |
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<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> |
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#### Description |
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GGML Format model files for [This project](https://huggingface.co./AlpachinoNLP/Baichuan-7B-Instruction). |
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### inference |
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```python |
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import ctransformers |
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from ctransformers import AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, |
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gpu_layers=32, model_type="llama") |
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manual_input: str = "Tell me about your last dream, please." |
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llm(manual_input, |
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max_new_tokens=256, |
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temperature=0.9, |
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top_p= 0.7) |
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``` |
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# Original model card |
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# Baichuan-7B-Instruction |
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<!-- Provide a quick summary of what the model is/does. --> |
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## 介绍 |
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Baichuan-7B-Instruction 为 Baichuan-7B 系列模型进行指令微调后的版本，预训练模型可见 [Baichuan-7B](https://huggingface.co./baichuan-inc/Baichuan-7B)。 |
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## Demo |
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如下是一个使用 gradio 的模型 demo |
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```python |
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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("AlpachinoNLP/Baichuan-7B-Instruction",trust_remote_code=True,use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-7B-Instruction",trust_remote_code=True ).half() |
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model.cuda() |
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def generate(histories, max_new_tokens=2048, do_sample = True, top_p = 0.95, temperature = 0.35, repetition_penalty=1.1): |
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prompt = "" |
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for history in histories: |
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history_with_identity = "\nHuman:" + history[0] + "\n\nAssistant:" + history[1] |
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prompt += history_with_identity |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device) |
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outputs = model.generate( |
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input_ids = input_ids, |
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max_new_tokens=max_new_tokens, |
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early_stopping=True, |
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do_sample=do_sample, |
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top_p=top_p, |
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temperature=temperature, |
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repetition_penalty=repetition_penalty, |
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) |
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rets = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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generate_text = rets[0].replace(prompt, "") |
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return generate_text |
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with gr.Blocks() as demo: |
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chatbot = gr.Chatbot() |
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msg = gr.Textbox() |
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clear = gr.Button("clear") |
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def user(user_message, history): |
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return "", history + [[user_message, ""]] |
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def bot(history): |
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print(history) |
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bot_message = generate(history) |
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history[-1][1] = bot_message |
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return history |
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( |
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bot, chatbot, chatbot |
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) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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if __name__ == "__main__": |
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demo.launch(server_name="0.0.0.0") |
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``` |
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## 量化部署 |
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Baichuan-7B 支持 int8 和 int4 量化，用户只需在推理代码中简单修改两行即可实现。请注意，如果是为了节省显存而进行量化，应加载原始精度模型到 CPU 后再开始量化；避免在 `from_pretrained` 时添加 `device_map='auto'` 或者其它会导致把原始精度模型直接加载到 GPU 的行为的参数。 |
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使用 int8 量化 (To use int8 quantization): |
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```python |
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-7B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) |
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model = model.quantize(8).cuda() |
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``` |
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同样的，如需使用 int4 量化 (Similarly, to use int4 quantization): |
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```python |
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model = AutoModelForCausalLM.from_pretrained("AlpachinoNLP/Baichuan-7B-Instruction", torch_dtype=torch.float16, trust_remote_code=True) |
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model = model.quantize(4).cuda() |
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``` |
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## 训练详情 |
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数据集：https://huggingface.co./datasets/shareAI/ShareGPT-Chinese-English-90k。 |
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硬件：8*A40 |
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## 测评结果 |
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## [CMMLU](https://github.com/haonan-li/CMMLU) |
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| Model 5-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average | |
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| ---------------------------------------------------------- | :-------: | :--------: | :-------------: | :------: | :------------: | :------: | |
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| Baichuan-7B | 34.4 | 47.5 | 47.6 | 46.6 | 44.3 | 44.0 | |
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| Vicuna-13B | 31.8 | 36.2 | 37.6 | 39.5 | 34.3 | 36.3 | |
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| Chinese-Alpaca-Plus-13B | 29.8 | 33.4 | 33.2 | 37.9 | 32.1 | 33.4 | |
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| Chinese-LLaMA-Plus-13B | 28.1 | 33.1 | 35.4 | 35.1 | 33.5 | 33.0 | |
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| Ziya-LLaMA-13B-Pretrain | 29.0 | 30.7 | 33.8 | 34.4 | 31.9 | 32.1 | |
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| LLaMA-13B | 29.2 | 30.8 | 31.6 | 33.0 | 30.5 | 31.2 | |
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| moss-moon-003-base (16B) | 27.2 | 30.4 | 28.8 | 32.6 | 28.7 | 29.6 | |
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| Baichuan-13B-Base | 41.7 | 61.1 | 59.8 | 59.0 | 56.4 | 55.3 | |
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| Baichuan-13B-Chat | 42.8 | 62.6 | 59.7 | 59.0 | 56.1 | 55.8 | |
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| Baichuan-13B-Instruction | 44.50 | 61.16 | 59.07 | 58.34 | 55.55 | 55.61 | |
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| **Baichuan-7B-Instruction** | **34.68** | **47.38** | **47.13** | **45.11** | **44.51** | **43.57** | |
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| Model zero-shot | STEM | Humanities | Social Sciences | Others | China Specific | Average | |
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| ------------------------------------------------------------ | :-------: | :--------: | :-------------: | :-------: | :------------: | :-------: | |
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| [ChatGLM2-6B](https://huggingface.co./THUDM/chatglm2-6b) | 41.28 | 52.85 | 53.37 | 52.24 | 50.58 | 49.95 | |
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| [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) | 32.79 | 44.43 | 46.78 | 44.79 | 43.11 | 42.33 | |
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| [ChatGLM-6B](https://github.com/THUDM/GLM-130B) | 32.22 | 42.91 | 44.81 | 42.60 | 41.93 | 40.79 | |
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| [BatGPT-15B](https://arxiv.org/abs/2307.00360) | 33.72 | 36.53 | 38.07 | 46.94 | 38.32 | 38.51 | |
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| [Chinese-LLaMA-7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 26.76 | 26.57 | 27.42 | 28.33 | 26.73 | 27.34 | |
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| [MOSS-SFT-16B](https://github.com/OpenLMLab/MOSS) | 25.68 | 26.35 | 27.21 | 27.92 | 26.70 | 26.88 | |
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| [Chinese-GLM-10B](https://github.com/THUDM/GLM) | 25.57 | 25.01 | 26.33 | 25.94 | 25.81 | 25.80 | |
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| [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-7B) | 42.04 | 60.49 | 59.55 | 56.60 | 55.72 | 54.63 | |
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| [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-7B) | 37.32 | 56.24 | 54.79 | 54.07 | 52.23 | 50.48 | |
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| Baichuan-13B-Instruction | 42.56 | 62.09 | 60.41 | 58.97 | 56.95 | 55.88 | |
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| **Baichuan-7B-Instruction** | **33.94** | **46.31** | **47.73** | **45.84** | **44.88** | **43.53** | |
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> 说明：CMMLU 是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。我们直接使用其官方的[评测脚本](https://github.com/haonan-li/CMMLU)对模型进行评测。Model zero-shot 表格中 [Baichuan-13B-Chat](https://github.com/baichuan-inc/Baichuan-13B) 的得分来自我们直接运行 CMMLU 官方的评测脚本得到，其他模型的的得分来自于 [CMMLU](https://github.com/haonan-li/CMMLU/tree/master) 官方的评测结果. |
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### 英文能力评测 |
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除了中文榜单的测试，我们同样测试了模型在英文榜单 MMLU 上的能力。 |
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#### MMLU |
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[MMLU](https://arxiv.org/abs/2009.03300) 是一个包含了57种任务的英文评测数据集。 |
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我们采用了开源的[评测方案]((https://github.com/hendrycks/test)) , 评测结果如下: |
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| Model | Humanities | Social Sciences | STEM | Other | Average | |
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|----------------------------------------|-----------:|:---------------:|:----:|:-----:|:-------:| |
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| LLaMA-7B<sup>2</sup> | 34.0 | 38.3 | 30.5 | 38.1 | 35.1 | |
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| Falcon-7B<sup>1</sup> | - | - | - | - | 35.0 | |
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| mpt-7B<sup>1</sup> | - | - | - | - | 35.6 | |
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| ChatGLM-6B<sup>0</sup> | 35.4 | 41.0 | 31.3 | 40.5 | 36.9 | |
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| BLOOM 7B<sup>0</sup> | 25.0 | 24.4 | 26.5 | 26.4 | 25.5 | |
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| BLOOMZ 7B<sup>0</sup> | 31.3 | 42.1 | 34.4 | 39.0 | 36.1 | |
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| moss-moon-003-base (16B)<sup>0</sup> | 24.2 | 22.8 | 22.4 | 24.4 | 23.6 | |
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| moss-moon-003-sft (16B)<sup>0</sup> | 30.5 | 33.8 | 29.3 | 34.4 | 31.9 | |
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| Baichuan-7B<sup>0</sup> | 38.4 | 48.9 | 35.6 | 48.1 | 42.3 | |
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| **Baichuan-7B-Instruction(5-shot)** | **38.9** | **49.0** | **35.3** | **48.8** | **42.6** | |
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| **Baichuan-7B-Instruction(0-shot)** | **38.7** | **47.9** | **34.5** | **48.2** | **42.0** | |
