Update README.md

README.md

@@ -87,4 +87,58 @@ LoupGarou. (2024). deepseek-coder-6.7b-instruct-pythagora (Model). https://huggi
 
 ## Model Card Contact
 
-For questions, feedback, or concerns regarding this model, please contact LoupGarou through the GitHub repository: [MoonlightByte/Pythagora-LLM-Proxy](https://github.com/MoonlightByte/Pythagora-LLM-Proxy). You can open an issue or submit a pull request to discuss any aspects of the model or its usage within the Pythagora GPT Pilot application.
+For questions, feedback, or concerns regarding this model, please contact LoupGarou through the GitHub repository: [MoonlightByte/Pythagora-LLM-Proxy](https://github.com/MoonlightByte/Pythagora-LLM-Proxy). You can open an issue or submit a pull request to discuss any aspects of the model or its usage within the Pythagora GPT Pilot application.
+
+
+
+**Original model card: DeepSeek's Deepseek Coder 6.7B Instruct**
+
+**[🏠Homepage](https://www.deepseek.com/)** | **[🤖 Chat with DeepSeek Coder](https://coder.deepseek.com/)** | **[Discord](https://discord.gg/Tc7c45Zzu5)** | **[Wechat(微信)](https://github.com/guoday/assert/blob/main/QR.png?raw=true)**
+
+---
+
+### 1. Introduction of Deepseek Coder
+
+Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
+
+- **Massive Training Data**: Trained from scratch fon 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
+- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
+- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
+- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
+
+### 2. Model Summary
+
+deepseek-coder-6.7b-instruct is a 6.7B parameter model initialized from deepseek-coder-6.7b-base and fine-tuned on 2B tokens of instruction data.
+
+- **Home Page:** [DeepSeek](https://www.deepseek.com/)
+- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
+- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
+
+### 3. How to Use
+
+Here give some examples of how to use our model.
+
+#### Chat Model Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True).cuda()
+messages=[
+    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+# 32021 is the id of <|EOT|> token
+outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
+print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
+```
+
+### 4. License
+
+This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
+
+See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
+
+### 5. Contact
+
+If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).