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--- |
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license: other |
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license_name: deepseek |
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license_link: LICENSE |
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datasets: |
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- unalignment/spicy-3.1 |
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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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# Fine-tune of Deepseek 67B |
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Fine-tuned with jondurbin's unalignment/spicy-3.1 for 1 epoch. |
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### 1. Introduction of Deepseek LLM |
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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. |
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### 2. Model Summary |
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`deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch. |
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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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#### Text 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-67b-base" |
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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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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" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(result) |
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``` |
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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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