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README.md

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+---
+license: other
+license_name: deepseek
+license_link: LICENSE
+datasets:
+- unalignment/spicy-3.1
+---
+
+<p align="center">
+<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
+</p>
+<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>
+<hr>
+
+# Fine-tune of Deepseek 67B
+
+Fine-tuned with jondurbin's unalignment/spicy-3.1 for 1 epoch.
+
+
+### 1. Introduction of Deepseek LLM
+
+Introducing DeepSeek LLM, an advanced language model comprising 67 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.
+
+  
+### 2. Model Summary
+`deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch.
+- **Home Page:** [DeepSeek](https://deepseek.com/)
+- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
+- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
+
+
+### 3. How to Use
+Here give some examples of how to use our model.
+#### Text Completion
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_name = "deepseek-ai/deepseek-llm-67b-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+model.generation_config = GenerationConfig.from_pretrained(model_name)
+model.generation_config.pad_token_id = model.generation_config.eos_token_id
+
+text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+### 4. License
+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.
+
+See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
+
+### 5. Contact
+
+If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
+

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