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")# pip install -U transformers accelerate # 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
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README.md
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- **Dataset**: [codelion/sutra-10B](https://huggingface.co/datasets/codelion/sutra-10B) — 10B token pedagogical pretraining dataset
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- **Sutra Framework**: Generates structured educational content optimized for LLM pretraining
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## License
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Apache 2.0
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- **Dataset**: [codelion/sutra-10B](https://huggingface.co/datasets/codelion/sutra-10B) — 10B token pedagogical pretraining dataset
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- **Sutra Framework**: Generates structured educational content optimized for LLM pretraining
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## Citation
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```bibtex
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@article{sharma2026sutra,
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title={Scaling Pedagogical Pretraining: From Optimal Mixing to 10 Billion Tokens},
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author={Sharma, Asankhaya},
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year={2026},
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url={https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens}
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}
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```
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## License
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Apache 2.0
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