Instructions to use agentica-org/DeepCoder-1.5B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agentica-org/DeepCoder-1.5B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="agentica-org/DeepCoder-1.5B-Preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("agentica-org/DeepCoder-1.5B-Preview") model = AutoModelForCausalLM.from_pretrained("agentica-org/DeepCoder-1.5B-Preview") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use agentica-org/DeepCoder-1.5B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "agentica-org/DeepCoder-1.5B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agentica-org/DeepCoder-1.5B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/agentica-org/DeepCoder-1.5B-Preview
- SGLang
How to use agentica-org/DeepCoder-1.5B-Preview 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 "agentica-org/DeepCoder-1.5B-Preview" \ --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": "agentica-org/DeepCoder-1.5B-Preview", "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 "agentica-org/DeepCoder-1.5B-Preview" \ --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": "agentica-org/DeepCoder-1.5B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use agentica-org/DeepCoder-1.5B-Preview with Docker Model Runner:
docker model run hf.co/agentica-org/DeepCoder-1.5B-Preview
Update README.md
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README.md
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<a href="https://github.com/agentica-project/rllm" style="margin: 2px;">
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<img alt="Code" src="https://img.shields.io/badge/RLLM-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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<img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://x.com/Agentica_" style="margin: 2px;">
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| **DeepCoder-14B-Preview** | 45.6 | 57.9 | 60.6 |
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| **DeepSeek-R1-Distill-Qwen-14B** | 50.2 | 53.0 | 53.0 |
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A more detailed description of the training recipe can be found in our [blog post](https://www.google.com).
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## Evaluation
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- Our model is trained on top of [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
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- Our work is done as part of [Berkeley Sky Computing Lab](https://skycomputing.berkeley.edu/) and [Berkeley AI Research](https://bair.berkeley.edu/).
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## Citation
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```bibtex
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```
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<a href="https://github.com/agentica-project/rllm" style="margin: 2px;">
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<img alt="Code" src="https://img.shields.io/badge/RLLM-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://pretty-radio-b75.notion.site/DeepCoder-A-Fully-Open-Source-14B-Coder-at-O3-mini-Level-1cf81902c14680b3bee5eb349a512a51" target="_blank" style="margin: 2px;">
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<img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://x.com/Agentica_" style="margin: 2px;">
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| --- | --- | --- | --- |
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| **DeepCoder-14B-Preview** | 45.6 | 57.9 | 60.6 |
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| **DeepSeek-R1-Distill-Qwen-14B** | 50.2 | 53.0 | 53.0 |
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A more detailed description of the training recipe can be found in our [blog post](https://www.google.com).
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## Evaluation
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- Our model is trained on top of [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
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- Our work is done as part of [Berkeley Sky Computing Lab](https://skycomputing.berkeley.edu/) and [Berkeley AI Research](https://bair.berkeley.edu/).
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## Citation
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```bibtex
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@misc{deepcoder2025,
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title={DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level},
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author={Michael Luo, Sijun Tan, Roy Huang, Xiaoxiang Shi, Rachel Xin, Colin Cai, Ameen Patel, Alpay Ariyak, Qingyang Wu, Ce Zhang, Li Erran Li, Raluca Ada Popa, Ion Stoica, Tianjun Zhang},
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howpublished={\url{https://pretty-radio-b75.notion.site/DeepCoder-A-Fully-Open-Source-14B-Coder-at-O3-mini-Level-1cf81902c14680b3bee5eb349a512a51}},
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note={Notion Blog},
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year={2025}
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}
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```
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