Instructions to use HuggingFaceTB/SmolLM2-360M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/SmolLM2-360M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-360M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M") model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceTB/SmolLM2-360M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceTB/SmolLM2-360M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceTB/SmolLM2-360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceTB/SmolLM2-360M
- SGLang
How to use HuggingFaceTB/SmolLM2-360M 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 "HuggingFaceTB/SmolLM2-360M" \ --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": "HuggingFaceTB/SmolLM2-360M", "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 "HuggingFaceTB/SmolLM2-360M" \ --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": "HuggingFaceTB/SmolLM2-360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceTB/SmolLM2-360M with Docker Model Runner:
docker model run hf.co/HuggingFaceTB/SmolLM2-360M
Missmatch between SmolLM2-360M-intermediate-checkpoints and SmolLM2-360M performance
#9 opened 11 months ago
by
Tobi-r9
need clarification on number of checkpoints
#8 opened about 1 year ago
by
bedio
More Training Information Required
🔥 5
#7 opened about 1 year ago
by
jayan12k
Sentencepiece tokenizer
#6 opened over 1 year ago
by
bh4
B/c Size Mismatch, Cant use from transformers import LlamaForCausalLM as workaround.
1
#5 opened over 1 year ago
by
MartialTerran
Safetensors size mismatch.
5
#4 opened over 1 year ago
by
MartialTerran
Sample Model Script for bfloat16 downloads safetensors parameters files then declares mismatch in their dimensions.
1
#3 opened over 1 year ago
by
MartialTerran
Need Help to build a SmolLM2_360M_model.py
1
#2 opened over 1 year ago
by
MartialTerran
Reproducing Evaluation with lighteval
4
#1 opened over 1 year ago
by
PatrickHaller