Instructions to use efittschen/MuonGPT-100M_2750 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efittschen/MuonGPT-100M_2750 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="efittschen/MuonGPT-100M_2750", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("efittschen/MuonGPT-100M_2750", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use efittschen/MuonGPT-100M_2750 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "efittschen/MuonGPT-100M_2750" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "efittschen/MuonGPT-100M_2750", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/efittschen/MuonGPT-100M_2750
- SGLang
How to use efittschen/MuonGPT-100M_2750 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 "efittschen/MuonGPT-100M_2750" \ --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": "efittschen/MuonGPT-100M_2750", "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 "efittschen/MuonGPT-100M_2750" \ --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": "efittschen/MuonGPT-100M_2750", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use efittschen/MuonGPT-100M_2750 with Docker Model Runner:
docker model run hf.co/efittschen/MuonGPT-100M_2750
Download config.json from efittschen/MuonGPT-100M_2750: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/efittschen/MuonGPT-100M_2750/resolve/main/config.json
- Command line
-
hf download hf://efittschen/MuonGPT-100M_2750/config.json
-
curl -L -o config.json https://huggingface.co/efittschen/MuonGPT-100M_2750/resolve/main/config.json
410 Bytes
| { | |
| "architectures": [ | |
| "MuonGPTForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_nano_gpt.MuonGPTConfig", | |
| "AutoModelForCausalLM": "modeling_nano_gpt.MuonGPTForCausalLM" | |
| }, | |
| "block_size": 128, | |
| "eos_token_id": 2, | |
| "model_dim": 768, | |
| "model_type": "muon-gpt", | |
| "num_heads": 6, | |
| "num_layers": 12, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "vocab_size": 16000 | |
| } | |