Instructions to use Chat-Error/Test_01_DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chat-Error/Test_01_DPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chat-Error/Test_01_DPO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chat-Error/Test_01_DPO") model = AutoModelForCausalLM.from_pretrained("Chat-Error/Test_01_DPO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chat-Error/Test_01_DPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chat-Error/Test_01_DPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chat-Error/Test_01_DPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chat-Error/Test_01_DPO
- SGLang
How to use Chat-Error/Test_01_DPO 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 "Chat-Error/Test_01_DPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chat-Error/Test_01_DPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Chat-Error/Test_01_DPO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chat-Error/Test_01_DPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chat-Error/Test_01_DPO with Docker Model Runner:
docker model run hf.co/Chat-Error/Test_01_DPO
Download pytorch_model-00006-of-00014.bin from Chat-Error/Test_01_DPO: direct link, hf CLI and curl.
- Browser
- Download file 1.9 GB
-
https://huggingface.co/Chat-Error/Test_01_DPO/resolve/main/pytorch_model-00006-of-00014.bin
- Command line
-
hf download hf://Chat-Error/Test_01_DPO/pytorch_model-00006-of-00014.bin
-
curl -L -o pytorch_model-00006-of-00014.bin https://huggingface.co/Chat-Error/Test_01_DPO/resolve/main/pytorch_model-00006-of-00014.bin
1.9 GB
- Xet hash:
- 88738366aee9954849e8d54922beaf6b5407cf8ff08fd4a085e2d5906c8b12ff
- Size of remote file:
- 1.9 GB
- SHA256:
- 588d495e2e32c0b5cae04793e9dd5a99f74bcef25b253d5ce34b64e6d2c41bff
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