Text Generation
Transformers
Safetensors
PyTorch
English
gpt2
hardware-bus
memory-augmented
toolformer
slm
autonomous-agent
ssd-memory
edge-ai
text-generation-inference
Instructions to use AvinashRicky/AViGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvinashRicky/AViGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AvinashRicky/AViGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AvinashRicky/AViGPT") model = AutoModelForCausalLM.from_pretrained("AvinashRicky/AViGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AvinashRicky/AViGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AvinashRicky/AViGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AvinashRicky/AViGPT
- SGLang
How to use AvinashRicky/AViGPT 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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AvinashRicky/AViGPT with Docker Model Runner:
docker model run hf.co/AvinashRicky/AViGPT
docs: add official Zenodo DOI 10.5281/zenodo.22856047 and citation
Browse files
README.md
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Rather than increasing parameter count to memorize factual records inside dense neural weights, AViGPT separates syntactic reasoning from factual storage. It couples a compact 183M reasoning core with a dedicated local NVMe SSD hardware memory bus. The model emits explicit control tokens to pause inference, execute sub-millisecond SQLite FTS5 full-text lookups on local storage, inject verified records into context, and complete generations with verified factual precision.
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* **Author:** Avinash Ricky Yadlapalli ([@AvinashRicky](https://huggingface.co/AvinashRicky))
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* **Code Repository:** [GitHub - Avinashricky211/AviGPT](https://github.com/Avinashricky211/AviGPT)
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* **License:** MIT Open Source License
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title={AViGPT: Decoupling Neural Reasoning from Parametric Memory via a Sub-Millisecond Native NVMe Hardware Bus},
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author={Avinash Ricky Yadlapalli},
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year={2026},
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journal={
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-
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url={https://github.com/Avinashricky211/AviGPT}
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}
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```
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Rather than increasing parameter count to memorize factual records inside dense neural weights, AViGPT separates syntactic reasoning from factual storage. It couples a compact 183M reasoning core with a dedicated local NVMe SSD hardware memory bus. The model emits explicit control tokens to pause inference, execute sub-millisecond SQLite FTS5 full-text lookups on local storage, inject verified records into context, and complete generations with verified factual precision.
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* **Author:** Avinash Ricky Yadlapalli ([@AvinashRicky](https://huggingface.co/AvinashRicky))
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* **DOI:** [10.5281/zenodo.22856047](https://doi.org/10.5281/zenodo.22856047)
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* **Code Repository:** [GitHub - Avinashricky211/AviGPT](https://github.com/Avinashricky211/AviGPT)
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* **License:** MIT Open Source License
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title={AViGPT: Decoupling Neural Reasoning from Parametric Memory via a Sub-Millisecond Native NVMe Hardware Bus},
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author={Avinash Ricky Yadlapalli},
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year={2026},
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journal={Zenodo},
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doi={10.5281/zenodo.22856047},
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howpublished={\url{https://doi.org/10.5281/zenodo.22856047}},
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url={https://github.com/Avinashricky211/AviGPT}
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}
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```
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