Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use derek-thomas/falcon-v03-poe-RFA-falcon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derek-thomas/falcon-v03-poe-RFA-falcon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="derek-thomas/falcon-v03-poe-RFA-falcon") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("derek-thomas/falcon-v03-poe-RFA-falcon", dtype="auto") - PEFT
How to use derek-thomas/falcon-v03-poe-RFA-falcon with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use derek-thomas/falcon-v03-poe-RFA-falcon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "derek-thomas/falcon-v03-poe-RFA-falcon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "derek-thomas/falcon-v03-poe-RFA-falcon", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/derek-thomas/falcon-v03-poe-RFA-falcon
- SGLang
How to use derek-thomas/falcon-v03-poe-RFA-falcon 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 "derek-thomas/falcon-v03-poe-RFA-falcon" \ --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": "derek-thomas/falcon-v03-poe-RFA-falcon", "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 "derek-thomas/falcon-v03-poe-RFA-falcon" \ --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": "derek-thomas/falcon-v03-poe-RFA-falcon", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use derek-thomas/falcon-v03-poe-RFA-falcon with Docker Model Runner:
docker model run hf.co/derek-thomas/falcon-v03-poe-RFA-falcon
- Xet hash:
- 0656f9a264b5047686ecfef4b58936aa62bc9bc53b4b6680c561c0a875d19217
- Size of remote file:
- 5.69 kB
- SHA256:
- 8350d2745eb7eb465a922e3ce353b8e71469d0690aef848a5bafc76277f78fa3
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