Instructions to use deepseek-ai/deepseek-coder-1.3b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/deepseek-coder-1.3b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepseek-ai/deepseek-coder-1.3b-base 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-1.3b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-1.3b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/deepseek-ai/deepseek-coder-1.3b-base
- SGLang
How to use deepseek-ai/deepseek-coder-1.3b-base 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-1.3b-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-1.3b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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-1.3b-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-coder-1.3b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use deepseek-ai/deepseek-coder-1.3b-base with Docker Model Runner:
docker model run hf.co/deepseek-ai/deepseek-coder-1.3b-base
Commit History
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Upload tokenizer.json
The persisted `tokenizer.json` does not have the template processor for adding special tokens. `transformers` overrides the processor on load, but when loading `tokenizer.json` directly with the Rust tokenizers it's nice to have the processor there already (which worked so far in case of other models). This basically re-saves the tokenizer to match exactly what is loaded by `transformers`. --- Generated with: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") assert tokenizer.is_fast tokenizer.save_pretrained("...") ```e94f2b1
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upload models
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