Text Generation
Transformers
cenn
knowledge-distillation
transformer-free
language-modeling
recurrent-neural-network
TinyCeNN-LM Distilled
Transformer-free CeNN student distilled from arnir0/Tiny-LLM.
- Transformer layers remaining: 0
- CeNN recurrent steps: 7
- CeNN receptive field: 255 tokens
- Training tokens: 10,002,432
- Best student CE: 5.247066
- Teacher CE: 4.423090
- Teacher-gap recovery: 84.92%
Load with the TinyCeNN-LM GitHub package via build_cenn_student().
Model tree for vtava/TinyCeNN-LM-Distilled
Base model
arnir0/Tiny-LLM
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "vtava/TinyCeNN-LM-Distilled"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vtava/TinyCeNN-LM-Distilled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'