Instructions to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/deepseek-coder-6.7b-instruct-GGUF", filename="deepseek-coder-6.7b-instruct.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/deepseek-coder-6.7b-instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/deepseek-coder-6.7b-instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with Ollama:
ollama run hf.co/QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/deepseek-coder-6.7b-instruct-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/deepseek-coder-6.7b-instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/deepseek-coder-6.7b-instruct-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/deepseek-coder-6.7b-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/deepseek-coder-6.7b-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.deepseek-coder-6.7b-instruct-GGUF-Q4_K_M
List all available models
lemonade list
Create README.md
Browse files
README.md
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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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base_model: deepseek-ai/deepseek-coder-6.7b-instruct
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pipeline_tag: text-generation
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---
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# QuantFactory/deepseek-coder-6.7b-instruct-GGUF
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This is quantized version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) created using llama.cpp
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# Model Description
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<p align="center">
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<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/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://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
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<hr>
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### 1. Introduction of Deepseek Coder
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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.
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- **Massive Training Data**: Trained from scratch fon 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
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- **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.
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- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
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- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
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### 2. Model Summary
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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.
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- **Home Page:** [DeepSeek](https://deepseek.com/)
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- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
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- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.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 Model Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
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messages=[
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{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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# tokenizer.eos_token_id is the id of <|EOT|> token
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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=tokenizer.eos_token_id)
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
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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 Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
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See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/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 [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
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