# LLM experimentation management and tracking using HuggingFace and Weights and Bias

**URL:** <https://community.wandb.ai/t/llm-experimentation-management-and-tracking-using-huggingface-and-weights-and-bias/4852>\
**Category:** Show the Community!\
**Created:** [August 5, 2023, 3:41am UTC](https://community.wandb.ai/t/llm-experimentation-management-and-tracking-using-huggingface-and-weights-and-bias/4852 "2023-08-05T03:41:40Z")\
**Posts on this page:** 1\
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**Author:** ![anindya](https://avatars.discourse-cdn.com/v4/letter/a/59ef9b/32.png) [@anindya](https://community.wandb.ai/u/anindya)\
**Post date:** [August 5, 2023, 3:41am UTC](https://community.wandb.ai/t/llm-experimentation-management-and-tracking-using-huggingface-and-weights-and-bias/4852/1 "2023-08-05T03:41:40Z")

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Fine tuning LLMs for domain specific tasks like classification (one some private dataset) is not easy. We have to understand lot of concepts and it I have struggled a lot to find a proper way to fine tune and evaluate our fine tuned models on tasks like classification.

- How a dataset can help a model to achieve to provide refined results
- how can we contraint our model’s output
- how to construct dataset with external prompts so that we can cast a language completion problem to a classification like problem
- how to fine tune a 7B model on a consumer gpu
- common problems while loading a peft model (saved in local) during inference
- how to build an overall analytical pipeline to asses LLM’s performance on quality, speed, and reliability
- Other different insights and best practices.

My latest blog covers it all, and being a three part blog series, more to come. In this blog I shared all the potential common best practices for fine tuning a large language models using Hugging Face and utilize Weights and Bias to effectively manage our experimentation and track our model based on different performance parameters.

Please do check out here

> <https://twitter.com/AnindyadeepS/status/1687538071527424023>