Text Generation
Transformers
Safetensors
llama
feature-extraction
conversational
custom_code
text-generation-inference
Instructions to use ByteDance-Seed/Stable-DiffCoder-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ByteDance-Seed/Stable-DiffCoder-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ByteDance-Seed/Stable-DiffCoder-8B-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Stable-DiffCoder-8B-Instruct", trust_remote_code=True) model = AutoModel.from_pretrained("ByteDance-Seed/Stable-DiffCoder-8B-Instruct", trust_remote_code=True) 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 ByteDance-Seed/Stable-DiffCoder-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ByteDance-Seed/Stable-DiffCoder-8B-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": "ByteDance-Seed/Stable-DiffCoder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ByteDance-Seed/Stable-DiffCoder-8B-Instruct
- SGLang
How to use ByteDance-Seed/Stable-DiffCoder-8B-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 "ByteDance-Seed/Stable-DiffCoder-8B-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": "ByteDance-Seed/Stable-DiffCoder-8B-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 "ByteDance-Seed/Stable-DiffCoder-8B-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": "ByteDance-Seed/Stable-DiffCoder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ByteDance-Seed/Stable-DiffCoder-8B-Instruct with Docker Model Runner:
docker model run hf.co/ByteDance-Seed/Stable-DiffCoder-8B-Instruct
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README.md
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| WeDLM-8B-Chat | 80.5 | 73.8 | 70.5 | - | - |
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| Stable-DiffCoder-8B-Instruct | **86.6** | **82.3** |**85.7**|**72.8**| **54.8** |
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For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Stable-DiffCoder/blob/master/
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## License
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| WeDLM-8B-Chat | 80.5 | 73.8 | 70.5 | - | - |
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| Stable-DiffCoder-8B-Instruct | **86.6** | **82.3** |**85.7**|**72.8**| **54.8** |
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For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Stable-DiffCoder/blob/master/Stable_DiffCoder.pdf).
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## License
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