Instructions to use Andrew0425/AgenticASR-Refiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andrew0425/AgenticASR-Refiner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Andrew0425/AgenticASR-Refiner") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Andrew0425/AgenticASR-Refiner") model = AutoModelForCausalLM.from_pretrained("Andrew0425/AgenticASR-Refiner", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Andrew0425/AgenticASR-Refiner with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Andrew0425/AgenticASR-Refiner" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Andrew0425/AgenticASR-Refiner", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Andrew0425/AgenticASR-Refiner
- SGLang
How to use Andrew0425/AgenticASR-Refiner 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 "Andrew0425/AgenticASR-Refiner" \ --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": "Andrew0425/AgenticASR-Refiner", "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 "Andrew0425/AgenticASR-Refiner" \ --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": "Andrew0425/AgenticASR-Refiner", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Andrew0425/AgenticASR-Refiner with Docker Model Runner:
docker model run hf.co/Andrew0425/AgenticASR-Refiner
| {"version": 3, "repo_id": "MuyuanJ/CPM-v2", "files": {"chat_template.jinja|1782718720.653171|9062": {"hash": "7451a05cf1e28a79d97d7c0bc951028c0b1915119bf9046acd06a0e3d931f47c", "size": 9062, "status": "c"}, "generation_config.json|1782718720.6545362|220": {"hash": "e7feb60aa6fc1f051c1ac1b780a21598b067f196ba7205e304c024703078af99", "size": 220, "status": "c"}, "tokenizer_config.json|1782718728.8509912|580": {"hash": "5f7439fd04b43e98d6ec7ed386367f5c464d6bd1b52eb1f5f4de29eed11c0fbc", "size": 580, "status": "c"}, "config.json|1782718720.653837|726": {"hash": "1e2212f0a509a6f7746b3adbff9d0f23d3807e803d55c2e173eb7ff1617ddf9f", "size": 726, "status": "c"}, "tokenizer.json|1782718728.8504515|9894271": {"hash": "3e065a558a034185fe299917b398685c1facd0169a9eea1e629eb30c171fed81", "size": 9894271, "status": "c"}, "model.safetensors|1782718728.7795076|4322556448": {"hash": "1a9635c8f288648c5ec3c9d8572caaa53295242c90f94f1655e07ef2b31d907d", "size": 4322556448, "status": "c"}}} |