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README.md ADDED
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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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+
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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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+
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+ # Fine-tune of Deepseek 67B
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+
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+ Fine-tuned with jondurbin's unalignment/spicy-3.1 for 1 epoch.
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+
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+
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+ ### 1. Introduction of Deepseek LLM
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+
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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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+
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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ### 5. Contact
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+
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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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+
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+ {
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+ "_name_or_path": "/aiml/models/deepseek-llm-67b-base",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 8192,
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+ "initializer_range": 0.02,
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+ "model_type": "llama",
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+ "transformers_version": "4.34.1",
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+ "use_cache": true,
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+ "vocab_size": 102400
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+ }
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+ "unk_token": null,
133
+ "use_default_system_prompt": true
134
+ }