Text Generation
Transformers
Safetensors
English
Chinese
llama
conversational
text-generation-inference
Instructions to use infly/OpenCoder-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use infly/OpenCoder-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="infly/OpenCoder-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("infly/OpenCoder-8B-Instruct") model = AutoModelForCausalLM.from_pretrained("infly/OpenCoder-8B-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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use infly/OpenCoder-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "infly/OpenCoder-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": "infly/OpenCoder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/infly/OpenCoder-8B-Instruct
- SGLang
How to use infly/OpenCoder-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 "infly/OpenCoder-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": "infly/OpenCoder-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 "infly/OpenCoder-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": "infly/OpenCoder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use infly/OpenCoder-8B-Instruct with Docker Model Runner:
docker model run hf.co/infly/OpenCoder-8B-Instruct
Update README.md
Browse files
README.md
CHANGED
|
@@ -27,7 +27,8 @@ datasets:
|
|
| 27 |
π <a href="https://opencoder-llm.github.io/">Home Page</a>   |
|
| 28 |
   π€ <a href="https://huggingface.co/collections/infly/opencoder-672cec44bbb86c39910fb55e">Model</a>   |
|
| 29 |
   π <a href="https://huggingface.co/collections/OpenCoder-LLM/opencoder-datasets-672e6db6a0fed24bd69ef1c2">Dataset</a>   |
|
| 30 |
-
   π<a href="https://arxiv.org/abs/2411.04905">Paper</a>  
|
|
|
|
| 31 |
</p>
|
| 32 |
|
| 33 |
|
|
|
|
| 27 |
π <a href="https://opencoder-llm.github.io/">Home Page</a>   |
|
| 28 |
   π€ <a href="https://huggingface.co/collections/infly/opencoder-672cec44bbb86c39910fb55e">Model</a>   |
|
| 29 |
   π <a href="https://huggingface.co/collections/OpenCoder-LLM/opencoder-datasets-672e6db6a0fed24bd69ef1c2">Dataset</a>   |
|
| 30 |
+
   π<a href="https://arxiv.org/abs/2411.04905">Paper</a>   |
|
| 31 |
+
   π<a href="https://huggingface.co/spaces/OpenCoder-LLM/OpenCoder-8B-Instruct">Demo</a>  
|
| 32 |
</p>
|
| 33 |
|
| 34 |
|