Instructions to use colinglab/CLASS_IT-140M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use colinglab/CLASS_IT-140M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="colinglab/CLASS_IT-140M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("colinglab/CLASS_IT-140M") model = AutoModelForCausalLM.from_pretrained("colinglab/CLASS_IT-140M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use colinglab/CLASS_IT-140M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "colinglab/CLASS_IT-140M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "colinglab/CLASS_IT-140M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/colinglab/CLASS_IT-140M
- SGLang
How to use colinglab/CLASS_IT-140M 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 "colinglab/CLASS_IT-140M" \ --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": "colinglab/CLASS_IT-140M", "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 "colinglab/CLASS_IT-140M" \ --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": "colinglab/CLASS_IT-140M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use colinglab/CLASS_IT-140M with Docker Model Runner:
docker model run hf.co/colinglab/CLASS_IT-140M
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Evaluation
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### Testing Data, Factors & Metrics
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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CLASS-IT is a 140M parameter language model based on the LLaMA architecture.
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The model is first pre-trained for 8 epochs on a cleaned version of the BabyLM Challenge strict track dataset.
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After pre-training, the model is instruction-tuned on two additional datasets (8.7M words total) for 10 epochs :
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- a conversational dataset derived from Switchboard, and
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- an educational dataset based on an augmented version of Simple English Wikipedia (to be released soon).
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The resulting model is designed to support interactive and educational use cases, combining general language understanding with dialog and explanatory capabilities.
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Evaluation
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The model has been submitted to the 2025 BabyLM Challenge – Interaction Track:
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https://huggingface.co/spaces/BabyLM-community/babylm-leaderboard-2025-all-tasks
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### Testing Data, Factors & Metrics
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