Text Generation
Transformers
Safetensors
English
llama
pretraining
educational
pedagogical
sutra
smollm2
Eval Results (legacy)
text-generation-inference
Instructions to use codelion/SmolLM2-70M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codelion/SmolLM2-70M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codelion/SmolLM2-70M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codelion/SmolLM2-70M") model = AutoModelForCausalLM.from_pretrained("codelion/SmolLM2-70M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codelion/SmolLM2-70M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codelion/SmolLM2-70M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codelion/SmolLM2-70M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codelion/SmolLM2-70M
- SGLang
How to use codelion/SmolLM2-70M 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 "codelion/SmolLM2-70M" \ --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": "codelion/SmolLM2-70M", "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 "codelion/SmolLM2-70M" \ --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": "codelion/SmolLM2-70M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codelion/SmolLM2-70M with Docker Model Runner:
docker model run hf.co/codelion/SmolLM2-70M
Download model.safetensors from codelion/SmolLM2-70M: direct link, hf CLI and curl.
- Browser
- Download file 138 MB
-
https://huggingface.co/codelion/SmolLM2-70M/resolve/main/model.safetensors
- Command line
-
hf download hf://codelion/SmolLM2-70M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/codelion/SmolLM2-70M/resolve/main/model.safetensors
138 MB
- Xet hash:
- a66590e8f86f2a3bb76d8c1c1ccf30a359ace27d2a24e456173dddf4aa8b8bd9
- Size of remote file:
- 138 MB
- SHA256:
- e777a572b1103d8b91543c1e2bdb632d1aeec9bb3879fac90a27b4ce45c92a17
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