tiny-GptOssForCausalLM Quantized GGUF

tiny-GptOssForCausalLM Quantized GGUF

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🧾 Hash-sum — b618c2fd18b6c3d32478f0463dcd8700 • 🗓 Updated on: 2026-06-26
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  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

tiny-GptOssForCausalLM is a compact, open‑source causal language model designed for efficient inference on consumer hardware. Built on a reduced transformer architecture, it retains strong performance on a variety of NLP tasks while requiring minimal memory footprint. The model leverages a shared embedding layer and grouped‑query attention to further reduce computational load, making it ideal for edge devices and research prototyping. A comparison table highlights its parameters, training tokens, and benchmark scores against similar small models:

Model Parameters Training Tokens Avg. Perplexity
tiny-GptOssForCausalLM 125M 1.5T 21.3
GPT‑Neo 125M 125M 1.0T 20.9
LLaMA‑2 7B 7B 2.0T 18.5

Developers can fine‑tune it using standard Hugging Face pipelines, benefiting from its permissive license and community‑driven improvements.

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