Instructions to use deepseek-ai/deepseek-coder-33b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/deepseek-coder-33b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/deepseek-coder-33b-instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct") model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use deepseek-ai/deepseek-coder-33b-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/deepseek-coder-33b-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-33b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/deepseek-coder-33b-instruct
- SGLang
How to use deepseek-ai/deepseek-coder-33b-instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "deepseek-ai/deepseek-coder-33b-instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-33b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "deepseek-ai/deepseek-coder-33b-instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-33b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/deepseek-coder-33b-instruct with Docker Model Runner:
docker model run hf.co/deepseek-ai/deepseek-coder-33b-instruct
tokenizer.model
Hi! please post tokenizer.model
We are working on this
yes very much please do this and allow exl2's to be freely generated
is there any news on this tokenizer.model?
Please - We really need the tokenizer.model
Thank you for your interest in DeepSeek Coder. Currently we can not provide a sentencepiece model (tokenizer.model) that achieves the same output as the existing HuggingFace Tokenizer. We are working hard to help the model quantization community directly support Huggingface's Tokenizer, and updates on our progress will be timely synced to https://github.com/deepseek-ai/DeepSeek-Coder#7-qa.
thanks for the update. Hopefully you take all the interest in the spirit it is given, as support for a fantastic achievement.