Text Generation
Transformers
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use Ponce-01/DFEP-05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ponce-01/DFEP-05 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ponce-01/DFEP-05") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ponce-01/DFEP-05", device_map="auto") - PEFT
How to use Ponce-01/DFEP-05 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ponce-01/DFEP-05 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ponce-01/DFEP-05" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ponce-01/DFEP-05", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ponce-01/DFEP-05
- SGLang
How to use Ponce-01/DFEP-05 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 "Ponce-01/DFEP-05" \ --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": "Ponce-01/DFEP-05", "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 "Ponce-01/DFEP-05" \ --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": "Ponce-01/DFEP-05", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Ponce-01/DFEP-05 with Docker Model Runner:
docker model run hf.co/Ponce-01/DFEP-05
Download training_args.bin from Ponce-01/DFEP-05: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Ponce-01/DFEP-05/resolve/main/training_args.bin
- Command line
-
hf download hf://Ponce-01/DFEP-05/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ponce-01/DFEP-05/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 2150d0ea98c630d3342aa17ed52bb4186c89473e1e3b3d5d0303ece09ca512b6
- Size of remote file:
- 4.92 kB
- SHA256:
- b48616edb7995550a027fc0ba976fa1c83bde52ee5d36d0b884282a40ce0042d
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