Text Generation
Transformers
Safetensors
English
swa_lm
custom_code
small-language-model
hybrid-attention
sliding-window-attention
muon
Instructions to use User01110/100M-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use User01110/100M-exp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="User01110/100M-exp", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("User01110/100M-exp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use User01110/100M-exp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "User01110/100M-exp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "User01110/100M-exp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/User01110/100M-exp
- SGLang
How to use User01110/100M-exp 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 "User01110/100M-exp" \ --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": "User01110/100M-exp", "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 "User01110/100M-exp" \ --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": "User01110/100M-exp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use User01110/100M-exp with Docker Model Runner:
docker model run hf.co/User01110/100M-exp
Download config.json from User01110/100M-exp: direct link, hf CLI and curl.
- Browser
- Download file 653 Bytes
-
https://huggingface.co/User01110/100M-exp/resolve/main/config.json
- Command line
-
hf download hf://User01110/100M-exp/config.json
-
curl -L -o config.json https://huggingface.co/User01110/100M-exp/resolve/main/config.json
653 Bytes
| { | |
| "architectures": [ | |
| "SWAForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_swa.SWAConfig", | |
| "AutoModelForCausalLM": "modeling_swa.SWAForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "conv": 4, | |
| "d": 576, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 2, | |
| "full": [ | |
| 6, | |
| 14 | |
| ], | |
| "h": 9, | |
| "hd": 64, | |
| "hidden_size": 576, | |
| "hope": true, | |
| "kv": 3, | |
| "l": 20, | |
| "m": 1792, | |
| "max_position_embeddings": 8192, | |
| "model_type": "swa_lm", | |
| "num_hidden_layers": 20, | |
| "pad_token_id": 2, | |
| "seq": 8192, | |
| "theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "v": 32000, | |
| "vocab_size": 32000, | |
| "window": 512 | |
| } |