73 lines
3.2 KiB
Markdown
73 lines
3.2 KiB
Markdown
---
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license: other
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license_name: deepseek
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license_link: LICENSE
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---
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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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<hr>
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### 1. Introduction of Deepseek LLM
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Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
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### 2. Model Summary
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`deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data.
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- **Home Page:** [DeepSeek](https://deepseek.com/)
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- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
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- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
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### 3. 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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```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-llm-7b-chat"
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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": "Who are you?"}
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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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### 4. License
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This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
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See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
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### 5. 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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