transformers safetensors mistral text-generation instruct finetune chatml synthetic data distillation dpo rlhf conversational en dataset:mlabonne/chatml_dpo_pairs base_model:wenqiglantz/MistralTrinity-7B-slerp license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
base_model: wenqiglantz/MistralTrinity-7B-slerp
tags:
- mistral
- instruct
- finetune
- chatml
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---

# MistralTrinity-7B-slerp-dpo

Inspired by @mlabonne's blog post [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac), this model was fine-tuned with DPO (Direct Preference Optimization) on base model `MistralTrinity-7B-slerp`, which is a merged model for `mistralai/Mistral-7B-Instruct-v0.2` and `jan-hq/trinity-v1`, using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. 

The code to train this model is available on [Google Colab](https://colab.research.google.com/github/wenqiglantz/llmops/blob/main/Fine_tune_MistralTrinity_7B_slerp_with_DPO.ipynb) and [GitHub](https://github.com/wenqiglantz/llmops/blob/main/Fine_tune_MistralTrinity_7B_slerp_with_DPO.ipynb). 

It required an A100 GPU for over an hour.

Check out fine-tuning run details on [Weights & Biases](https://wandb.ai/wenqiglantz/huggingface/runs/sxbgd33f).
