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metadata
license: apache-2.0
model-index:
  - name: Rubra-Qwen2-7B-Instruct
    results:
      - task:
          type: text-generation
        dataset:
          type: MMLU
          name: MMLU
        metrics:
          - type: 5-shot
            value: 68.88
            verified: false
      - task:
          type: text-generation
        dataset:
          type: GPQA
          name: GPQA
        metrics:
          - type: 0-shot
            value: 30.36
            verified: false
      - task:
          type: text-generation
        dataset:
          type: GSM-8K
          name: GSM-8K
        metrics:
          - type: 8-shot, CoT
            value: 75.82
            verified: false
      - task:
          type: text-generation
        dataset:
          type: MATH
          name: MATH
        metrics:
          - type: 4-shot, CoT
            value: 28.72
            verified: false
      - task:
          type: text-generation
        dataset:
          type: MT-bench
          name: MT-bench
        metrics:
          - type: GPT-4 as Judge
            value: 8.08
            verified: false
tags:
  - function-calling
  - tool-calling
  - agentic
  - rubra
  - conversational
language:
  - en
  - zh

Qwen2 7B Instruct GGUF

Original model: rubra-ai/Qwen2-7B-Instruct

Model description

The model is the result of further post-training Qwen/Qwen2-7B-Instruct. It is capable of complex multi-turn tool/function calling.

Training

The model was post-trained (freeze tuned & DPO) on a proprietary dataset consisting of diverse function calling, chat, and instruct data.

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 Alibaba Cloud for the model.

Contact Information

For questions or comments about the model, please reach out to the rubra team.

Citation

If you use this work, please cite it as:

@misc {rubra_ai_2024,
    author       = { Sanjay Nadhavajhala and Yingbei Tong },
    title        = { Rubra-Qwen2-7B-Instruct },
    year         = 2024,
    url          = { https://huggingface.co/rubra-ai/Qwen2-7B-Instruct },
    doi          = { 10.57967/hf/2683 },
    publisher    = { Hugging Face }
}