---

language:

• en license: gemma library_name: llama.cpp pipeline_tag: text-generation tags:
• gguf
• gemma
• gemma-3
• instruction-tuned
• quantized
• llama-cpp
• local-llm
• rag base_model:
• google/gemma-3-270m-it

---

Gipity 3 270M IT — Q4_K_M (GGUF)

gipity-3-270m-it-Q4_K_M.gguf is a quantized GGUF deployment artifact used by the Gipity application. The application source indicates that this model is loaded locally through llama-cpp-python and is based on google/gemma-3-270m-it.

This repository is intended to host the GGUF model file used by the application, while the main application code is hosted separately on GitHub:

• Main application repository: https://github.com/is-leeroy-jenkins/Gipity.git
• Model file: gipity-3-270m-it-Q4_K_M.gguf
• Base model: google/gemma-3-270m-it
• Primary runtime for this artifact: llama.cpp / llama-cpp-python
• Role in the application: local fallback model for text generation when the primary provider is unavailable or when a local path is preferred

What Gipity Uses This Model For

Based on the attached source files and the intended application design, Gipity uses a local GGUF model as a fallback text-generation path inside a broader multimodal application that is designed primarily to provide access to OpenAI GPT-5.x capabilities.

At a high level, Gipity is designed around multimodal workflows that include:

• text generation and chat
• image and vision workflows
• audio workflows
• embeddings
• file handling
• vector stores
• prompt-engineering utilities

Within that architecture, gipity-3-270m-it-Q4_K_M.gguf serves as a lightweight local option for text generation when a local model is preferred or when the primary remote provider path is not being used.

The application configuration points to the following default local model path:

models/gipity-3-270m-it-Q4_K_M.gguf

The runtime loads the model through Llama(...) from llama_cpp, and the application defaults to a 4096-token context window for local inference.

Intended Usage

This model is intended for local inference in GGUF-compatible runtimes, especially:

• llama.cpp
• llama-cpp-python
• desktop applications or Streamlit applications that load GGUF models directly

In the context of Gipity, this repository should be understood as the model-hosting companion to the main GitHub application repository rather than as the complete application itself.

Typical usage scenarios include:

• lightweight local assistants
• document-grounded Q&A
• prompt-based drafting and summarization
• local fallback inference for a larger multimodal GPT-5.x application
• experimentation with small-footprint local Gemma-family deployments

Example: llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="gipity-3-270m-it-Q4_K_M.gguf",
    n_ctx=4096,
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain what this model is for."},
    ]
)

print(response["choices"][0]["message"]["content"])

Example: llama.cpp

./llama-cli \
  -m gipity-3-270m-it-Q4_K_M.gguf \
  -c 4096 \
  -p "Write a short description of Gipity."

Prompting Notes

Because this file is based on an instruction-tuned Gemma-family model, best results generally come from:

• clear task-oriented prompts
• concise system instructions
• grounded context when using RAG
• short to moderate generations for factual tasks

For document Q&A workflows, pair the model with retrieved context rather than relying on parametric memory alone.

Quantization Notes

This repository hosts a GGUF quantized artifact rather than an original full-precision checkpoint. The file name indicates a Q4_K_M quantization variant.

Quantization typically reduces model size and memory requirements, making local inference easier on consumer hardware, but it may also reduce generation quality relative to higher-precision variants.

Provenance and What Is Known

The following points are supported by the Gipity source files:

• the application expects a model file named gipity-3-270m-it-Q4_K_M.gguf
• the configured model path is models/gipity-3-270m-it-Q4_K_M.gguf
• inference is performed through llama-cpp-python
• the application uses a 4096-token context window by default
• the application combines local text generation with embedding-based retrieval components

What Is Not Claimed Here

This model card does not claim any of the following unless separate evidence is provided:

• a custom fine-tuning dataset
• a specific post-training alignment procedure
• benchmark results
• exact quantization tooling or conversion commands
• safety evaluations beyond those of the upstream model family

If you have those details, they should be added explicitly in a later revision.

Limitations

As a small quantized instruction model, this artifact may:

• hallucinate facts
• struggle with long multi-step reasoning
• lose fidelity on highly technical or domain-dense tasks
• perform worse than larger or less aggressively quantized models
• require careful retrieval support for document-heavy workflows

It should be treated as an assistive generation component, not as an authoritative source.

Safety and Responsible Use

Users should review outputs before acting on them, especially for:

• legal matters
• financial decisions
• medical or health-related questions
• employment or compliance workflows
• any task requiring high factual precision

Do not rely on model output as a substitute for professional judgment or verified source material.

Hardware Considerations

Because this is a small GGUF quantized model, it is suitable for lightweight local inference relative to larger checkpoints. Actual performance will depend on:

• runtime configuration
• CPU versus GPU offloading
• available RAM / VRAM
• context length
• batch size and thread settings

License and Upstream Terms

This artifact is based on google/gemma-3-270m-it. Use of this repository and any redistributed artifacts should comply with:

• the license and usage terms attached to the upstream Gemma model
• any additional redistribution requirements that apply to converted or quantized derivatives

Before publishing, confirm that your intended distribution of the GGUF file is consistent with the applicable upstream license terms.

Files

This repository is expected to contain:

README.md
gipity-3-270m-it-Q4_K_M.gguf

Relationship to the Main Gipity Repository

This repository is best used alongside the main Gipity codebase hosted on GitHub:

https://github.com/is-leeroy-jenkins/Gipity.git

The GitHub repository contains the application logic and user-facing features, while this repository is intended to host the GGUF model artifact used for local fallback inference.

Recommended Repository Description

A GGUF-hosted quantized local fallback model for the Gipity application, based on google/gemma-3-270m-it, intended for local inference with llama.cpp / llama-cpp-python, and used alongside the main Gipity application hosted on GitHub.

Acknowledgments

• Google for the upstream Gemma model family
• The llama.cpp and llama-cpp-python communities for GGUF-compatible local inference tooling
• The Gipity application source, which documents how this model is loaded and used in practice