Instructions to use TIGER-Lab/MAmmoTH-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/MAmmoTH-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TIGER-Lab/MAmmoTH-70B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/MAmmoTH-70B") model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/MAmmoTH-70B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TIGER-Lab/MAmmoTH-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TIGER-Lab/MAmmoTH-70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TIGER-Lab/MAmmoTH-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TIGER-Lab/MAmmoTH-70B
- SGLang
How to use TIGER-Lab/MAmmoTH-70B 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 "TIGER-Lab/MAmmoTH-70B" \ --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": "TIGER-Lab/MAmmoTH-70B", "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 "TIGER-Lab/MAmmoTH-70B" \ --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": "TIGER-Lab/MAmmoTH-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TIGER-Lab/MAmmoTH-70B with Docker Model Runner:
docker model run hf.co/TIGER-Lab/MAmmoTH-70B
Commit History
-
Update README.md
2c17820
-
Update README.md
e757a38
-
Update README.md
350ab0b
-
Create README.md
0473480
-
Add pytorch_model-00015-of-00015.bin
xiangyuec3f2dbe -
Add pytorch_model-00014-of-00015.bin
xiangyuefb728b9 -
Add pytorch_model-00013-of-00015.bin
xiangyue0a95b2d -
Add pytorch_model-00012-of-00015.bin
xiangyue8d748a1 -
Add pytorch_model-00011-of-00015.bin
xiangyued88e9ac -
Add pytorch_model-00010-of-00015.bin
xiangyue2cda366 -
Add pytorch_model-00009-of-00015.bin
xiangyue817d871 -
Add pytorch_model-00008-of-00015.bin
xiangyuef368438 -
Add pytorch_model-00007-of-00015.bin
xiangyue135519d -
Add pytorch_model-00006-of-00015.bin
xiangyuecb955d3 -
Add pytorch_model-00005-of-00015.bin
xiangyue0ce763f -
Add pytorch_model-00004-of-00015.bin
xiangyuea75026a -
Add pytorch_model-00003-of-00015.bin
xiangyuec3bb08b -
Add pytorch_model-00002-of-00015.bin
xiangyueed1dc3f -
Add pytorch_model-00001-of-00015.bin
xiangyue24e26b8 -
add other files
xiangyue3b133c0 -
initial commit
Xiang Yue14985ea