82 lines
3.4 KiB
Markdown
82 lines
3.4 KiB
Markdown
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---
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license: other
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license_name: deepseek
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license_link: https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL
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pipeline_tag: text-generation
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base_model: deepseek-ai/deepseek-math-7b-rl
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---
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# QuantFactory/deepseek-math-7b-rl-GGUF
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This is quantized version of [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl) created using llama.cpp
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# Model Description
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<p align="center">
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<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
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</p>
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<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
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<p align="center">
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<a href="https://arxiv.org/pdf/2402.03300.pdf"><b>Paper Link</b>👁️</a>
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</p>
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<hr>
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### 1. Introduction to DeepSeekMath
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See the [Introduction](https://github.com/deepseek-ai/DeepSeek-Math) for more details.
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### 2. How to Use
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Here give some examples of how to use our model.
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**Chat Completion**
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❗❗❗ **Please use chain-of-thought prompt to test DeepSeekMath-Instruct and DeepSeekMath-RL:**
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- English questions: **{question}\nPlease reason step by step, and put your final answer within \\boxed{}.**
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- Chinese questions: **{question}\n请通过逐步推理来解答问题，并把最终答案放置于\\boxed{}中。**
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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model_name = "deepseek-ai/deepseek-math-7b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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model.generation_config = GenerationConfig.from_pretrained(model_name)
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model.generation_config.pad_token_id = model.generation_config.eos_token_id
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messages = [
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{"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."}
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]
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input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
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result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
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print(result)
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```
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Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
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```
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User: {messages[0]['content']}
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Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
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Assistant:
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```
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**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
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### 3. License
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This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use.
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See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL) for more details.
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### 4. Contact
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If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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