Instructions to use HuggingFaceTB/SmolLM2-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/SmolLM2-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-1.7B") model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-1.7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceTB/SmolLM2-1.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceTB/SmolLM2-1.7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceTB/SmolLM2-1.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B
- SGLang
How to use HuggingFaceTB/SmolLM2-1.7B 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/SmolLM2-1.7B" \ --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/SmolLM2-1.7B", "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/SmolLM2-1.7B" \ --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/SmolLM2-1.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceTB/SmolLM2-1.7B with Docker Model Runner:
docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B
Unexpected behavior of models in terms of generation time
#9 opened 3 months ago
by
LinkaG
Add link to Neuron-optimized version
#8 opened 9 months ago
by
badaoui
Using Adapter/PEFT for finetuning a Subnet extracted from the SmolLM2 for Arduino Tool Calling
1
#7 opened over 1 year ago
by
MartialTerran
Extracting an optimized Arduino Tool-Calling Subnet from the SmolLM2 model.
#6 opened over 1 year ago
by
MartialTerran
Extracting subnets from the published SmolLM2 model for compute-efficient task performance on edge devices
#5 opened over 1 year ago
by
MartialTerran
Porting SmolLM2 to Arduino
1
#4 opened over 1 year ago
by
MartialTerran
Pure C++ version of the SmolLM2 model code for EDGE implementations
#3 opened over 1 year ago
by
MartialTerran
Pure Python version for local Inference operation on PC
❤️ 1
1
#2 opened over 1 year ago
by
MartialTerran