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---
license: apache-2.0
model-index:
- name: Rubra-Mistral-7B-Instruct-v0.2
results:
- task:
type: text-generation
dataset:
type: MMLU
name: MMLU
metrics:
- type: 5-shot
value: 58.9
verified: false
- task:
type: text-generation
dataset:
type: GPQA
name: GPQA
metrics:
- type: 0-shot
value: 29.91
verified: false
- task:
type: text-generation
dataset:
type: GSM-8K
name: GSM-8K
metrics:
- type: 8-shot, CoT
value: 34.12
verified: false
- task:
type: text-generation
dataset:
type: MATH
name: MATH
metrics:
- type: 4-shot, CoT
value: 8.36
verified: false
- task:
type: text-generation
dataset:
type: MT-bench
name: MT-bench
metrics:
- type: GPT-4 as Judge
value: 7.36
verified: false
tags:
- function-calling
- tool-calling
- agentic
---
# Rubra Mistral-7B-Instruct-v0.2
## Model description
The model is the result of further post-training [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). It is capable of complex tool/function calling.
## Training Data
The model was post-trained (freeze tuned & DPO) on a proprietary dataset consisting of diverse function calling, chat, and instruct data.
## How to use
You can use the model with the Hugging Face `transformers` and the rubra library [rubra-tools](https://github.com/rubra-ai/rubra-tools) as follows:
```
pip install rubra_tools torch==2.3.0 transformers
```
```python
TODO
```
## Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
## Framework Versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
## 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 Mistral for the model and LLaMA-Factory for training utilities.
## 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 = { Mistral-7B-Instruct-v0.2 },
year = 2024,
url = { https://huggingface.co/rubra-ai/Mistral-7B-Instruct-v0.2 },
doi = { 10.57967/hf/2641 },
publisher = { Hugging Face }
}
``` |