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gemma-1.1-2b-it-GGUF

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gemma-1.1-2b-it-GGUF
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---
license: gemma
model-index:
- name: Rubra-Meta-Llama-3-8B-Instruct
  results:
  - task:
      type: text-generation
    dataset:
      type: MMLU
      name: MMLU
    metrics:
    - type: 5-shot
      value: 38.85
      verified: false
  - task:
      type: text-generation
    dataset:
      type: GPQA
      name: GPQA
    metrics:
    - type: 0-shot
      value: 24.55
      verified: false
  - task:
      type: text-generation
    dataset:
      type: GSM-8K
      name: GSM-8K
    metrics:
    - type: 8-shot, CoT
      value: 6.14
      verified: false
  - task:
      type: text-generation
    dataset:
      type: MATH
      name: MATH
    metrics:
    - type: 4-shot, CoT
      value: 2.38
      verified: false
  - task:
      type: text-generation
    dataset:
      type: MT-bench
      name: MT-bench
    metrics:
    - type: GPT-4 as Judge
      value: 5.75
      verified: false
tags:
- function-calling
- tool-calling
- agentic
- rubra
- gemma
- conversational
language:
- en
---

# Gemma-1.1 2B Instruct GGUF

Original model: [rubra-ai/gemma-1.1-2b-it](https://huggingface.co/rubra-ai/gemma-1.1-2b-it)

## Model Description
Gemma-1.1-2B-IT is the result of post-training on the base model [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it). This model is designed for high performance in various instruction-following tasks and complex interactions, including multi-turn function calling and detailed conversations.

## Training Data
The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively.

## How to Use
Refer to https://docs.rubra.ai/inference/llamacpp for usage. Feel free to ask/open issues up in our Github repo: https://github.com/rubra-ai/rubra 

## Limitations and Bias

While the model performs well on a wide range of tasks, it may still produce biased or incorrect outputs. Users should exercise caution and critical judgment when using the model in sensitive or high-stakes applications. The model's outputs are influenced by the data it was trained on, which may contain inherent biases.

## Ethical Considerations

Users should ensure that the deployment of this model adheres to ethical guidelines and consider the potential societal impact of the generated text. Misuse of the model for generating harmful or misleading content is strongly discouraged.

## Acknowledgements

We would like to thank Google for the model.

## Contact Information

For questions or comments about the model, please reach out to [the rubra team](mailto:rubra@acorn.io).

## Citation

If you use this work, please cite it as:

```
@misc {rubra_ai_2024,
	author       = { Sanjay Nadhavajhala and Yingbei Tong },
	title        = { Rubra-Gemma-1.1-2B-IT },
	year         = 2024,
	url          = { https://huggingface.co/rubra-ai/gemma-1.1-2b-it },
	doi          = { 10.57967/hf/2681 },
	publisher    = { Hugging Face }
}