Text Generation
Transformers
Safetensors
llama
alignment-handbook
trl
dpo
Generated from Trainer
text-generation-inference
Instructions to use Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3") model = AutoModelForMultimodalLM.from_pretrained("Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3
- SGLang
How to use Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3 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 "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3" \ --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": "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3", "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 "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3" \ --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": "Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3 with Docker Model Runner:
docker model run hf.co/Minbyul/meditron-7b-dpo-full-wo-healthsearch_qa-ep3
- Xet hash:
- ad4ef6bbf16c678e45b50cfd934eea351b56a44e73c46a9ac72772fd58234d39
- Size of remote file:
- 4.95 GB
- SHA256:
- 046cd2ffc20b900723bab66d3b7d814961095e179cc0860f89dea881b6670115
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