Instructions to use tkhangg/Dual-Explain-2round-Q1_first-overlay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tkhangg/Dual-Explain-2round-Q1_first-overlay with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tkhangg0910/Merged-Dual-Explain-Stage_full-Q1_first") model = PeftModel.from_pretrained(base_model, "tkhangg/Dual-Explain-2round-Q1_first-overlay") - Transformers
How to use tkhangg/Dual-Explain-2round-Q1_first-overlay with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tkhangg/Dual-Explain-2round-Q1_first-overlay") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tkhangg/Dual-Explain-2round-Q1_first-overlay", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tkhangg/Dual-Explain-2round-Q1_first-overlay with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tkhangg/Dual-Explain-2round-Q1_first-overlay" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tkhangg/Dual-Explain-2round-Q1_first-overlay", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tkhangg/Dual-Explain-2round-Q1_first-overlay
- SGLang
How to use tkhangg/Dual-Explain-2round-Q1_first-overlay 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 "tkhangg/Dual-Explain-2round-Q1_first-overlay" \ --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": "tkhangg/Dual-Explain-2round-Q1_first-overlay", "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 "tkhangg/Dual-Explain-2round-Q1_first-overlay" \ --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": "tkhangg/Dual-Explain-2round-Q1_first-overlay", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tkhangg/Dual-Explain-2round-Q1_first-overlay with Docker Model Runner:
docker model run hf.co/tkhangg/Dual-Explain-2round-Q1_first-overlay
- Xet hash:
- 6d17fad73588885603a13319381b37868c9d491c56e01e5520d824862e05e3e3
- Size of remote file:
- 1.08 GB
- SHA256:
- 69899e69b6e63486e0b0e4b008c54b151cdc57c4bf8804b3e0e8183b5c77849a
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