Instructions to use codys12/MAmmoTH-phi-1_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codys12/MAmmoTH-phi-1_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codys12/MAmmoTH-phi-1_5", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codys12/MAmmoTH-phi-1_5", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("codys12/MAmmoTH-phi-1_5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codys12/MAmmoTH-phi-1_5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codys12/MAmmoTH-phi-1_5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codys12/MAmmoTH-phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codys12/MAmmoTH-phi-1_5
- SGLang
How to use codys12/MAmmoTH-phi-1_5 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 "codys12/MAmmoTH-phi-1_5" \ --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": "codys12/MAmmoTH-phi-1_5", "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 "codys12/MAmmoTH-phi-1_5" \ --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": "codys12/MAmmoTH-phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codys12/MAmmoTH-phi-1_5 with Docker Model Runner:
docker model run hf.co/codys12/MAmmoTH-phi-1_5
Download training_args.bin from codys12/MAmmoTH-phi-1_5: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/codys12/MAmmoTH-phi-1_5/resolve/main/training_args.bin
- Command line
-
hf download hf://codys12/MAmmoTH-phi-1_5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/codys12/MAmmoTH-phi-1_5/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- f8d03223784260b1022b20ee5c572377a3a11b02ec4752418d766db37d483ccd
- Size of remote file:
- 4.66 kB
- SHA256:
- f0217b5ddb12e73d138401bb7f4c59069d7cb88abd95709ad217eb69428efaff
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