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 }
}