Instructions to use yruning/backtrack_SFT_DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yruning/backtrack_SFT_DPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yruning/backtrack_SFT_DPO")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yruning/backtrack_SFT_DPO") model = AutoModelForCausalLM.from_pretrained("yruning/backtrack_SFT_DPO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yruning/backtrack_SFT_DPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yruning/backtrack_SFT_DPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yruning/backtrack_SFT_DPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yruning/backtrack_SFT_DPO
- SGLang
How to use yruning/backtrack_SFT_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 "yruning/backtrack_SFT_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": "yruning/backtrack_SFT_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 "yruning/backtrack_SFT_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": "yruning/backtrack_SFT_DPO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yruning/backtrack_SFT_DPO with Docker Model Runner:
docker model run hf.co/yruning/backtrack_SFT_DPO
| { | |
| "additional_special_tokens": [ | |
| "[RESET]", | |
| "[BACKTRACK]", | |
| "[Retrieval]", | |
| "[Relevance]", | |
| "[Support]", | |
| "[Utility]", | |
| "[THINK]", | |
| "[IsRel]", | |
| "[IsSup]", | |
| "[IsUse]", | |
| "[Retrieval]{Yes}", | |
| "[Retrieval]{No}", | |
| "[Retrieval]{Continue}", | |
| "[Relevance]{Relevant}", | |
| "[Relevance]{Irrelevant}", | |
| "[Support]{Fully}", | |
| "[Support]{Partially}", | |
| "[Support]{No}", | |
| "[Utility]{1}", | |
| "[Utility]{2}", | |
| "[Utility]{3}", | |
| "[Utility]{4}", | |
| "[Utility]{5}" | |
| ], | |
| "bos_token": { | |
| "content": "<bos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "content": "<eos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |