Text Generation
Transformers
cenn
tiny-llm
language-modeling
recurrent-neural-network
parameter-efficient
research
Instructions to use vtava/TinyCeNN-LM-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vtava/TinyCeNN-LM-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vtava/TinyCeNN-LM-Base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vtava/TinyCeNN-LM-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vtava/TinyCeNN-LM-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vtava/TinyCeNN-LM-Base" # 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-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vtava/TinyCeNN-LM-Base
- SGLang
How to use vtava/TinyCeNN-LM-Base 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 "vtava/TinyCeNN-LM-Base" \ --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": "vtava/TinyCeNN-LM-Base", "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 "vtava/TinyCeNN-LM-Base" \ --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": "vtava/TinyCeNN-LM-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vtava/TinyCeNN-LM-Base with Docker Model Runner:
docker model run hf.co/vtava/TinyCeNN-LM-Base
TinyCeNN-LM Base
CeNN residual adapter trained on top of arnir0/Tiny-LLM.
- CeNN recurrent steps: 4
- Context length: 256
- Training token budget: 10,000,000
- Health status: warning_no_improvement
- Initial eval loss: 4.1657339334487915
- Best eval loss: 4.160854339599609
- Best perplexity: 64.12628482834027
This model repository contains TinyCeNN adapter weights/configuration, tokenizer metadata, and the training report. Reconstruct the model with the TinyCeNN-LM GitHub code.
Model tree for vtava/TinyCeNN-LM-Base
Base model
arnir0/Tiny-LLM