Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use derek-thomas/falcon-v03-poe-FAR-falcon_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derek-thomas/falcon-v03-poe-FAR-falcon_backup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="derek-thomas/falcon-v03-poe-FAR-falcon_backup") 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-FAR-falcon_backup", device_map="auto") - PEFT
How to use derek-thomas/falcon-v03-poe-FAR-falcon_backup with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use derek-thomas/falcon-v03-poe-FAR-falcon_backup 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-FAR-falcon_backup" # 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-FAR-falcon_backup", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/derek-thomas/falcon-v03-poe-FAR-falcon_backup
- SGLang
How to use derek-thomas/falcon-v03-poe-FAR-falcon_backup 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-FAR-falcon_backup" \ --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-FAR-falcon_backup", "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-FAR-falcon_backup" \ --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-FAR-falcon_backup", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use derek-thomas/falcon-v03-poe-FAR-falcon_backup with Docker Model Runner:
docker model run hf.co/derek-thomas/falcon-v03-poe-FAR-falcon_backup
Download training_args.bin from derek-thomas/falcon-v03-poe-FAR-falcon_backup: direct link, hf CLI and curl.
- Browser
- Download file 5.69 kB
-
https://huggingface.co/derek-thomas/falcon-v03-poe-FAR-falcon_backup/resolve/main/training_args.bin
- Command line
-
hf download hf://derek-thomas/falcon-v03-poe-FAR-falcon_backup/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/derek-thomas/falcon-v03-poe-FAR-falcon_backup/resolve/main/training_args.bin
5.69 kB
- Xet hash:
- 566a8e5ce77ab43d5c08c7c970ad07ee97877d1e0dc321c0178b8526e01eff7f
- Size of remote file:
- 5.69 kB
- SHA256:
- 59e17ef544a06ee457a5e1b82d1ab88c5175ac3b71d63a5fccb6ec22fae89d83
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