README.md

---
license: apache-2.0
inference: false
datasets:
- PengQu/langchain-MRKL-finetune
- fnlp/moss-003-sft-data
- anon8231489123/ShareGPT_Vicuna_unfiltered
---


**NOTE: This "delta model" cannot be used directly.**  
Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights.  
See https://github.com/pengqu123/vicuna-13b-delta-finetuned-langchain-MRKL for instructions.
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# vicuna-13b-finetuned-langchain-MRKL

## Model details

**Model type:**
vicuna-13b-finetuned-langchain-MRKL is an open-source chatbot trained by fine-tuning vicuna-13b on 15 examples with langchain-MRKL format.

**Where to send questions or comments about the model:**
https://github.com/pengqu123/vicuna-13b-delta-finetuned-langchain-MRKL/issues

## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.

**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

## Training dataset
train only one epoch on mix data (sharegpt + 32*my.json + moss-003-sft-data)

## Evaluation
demo code: https://github.com/pengqu123/vicuna-13b-delta-finetuned-langchain-MRKL/blob/main/demo.ipynb
No evaluation set. Because we don't improve the ability of model. we just make model fit langchain-MRKL strictly.
We just want to show vicuna-13b's powerful ability about thinking and action.
This is the first step. We hope if we get more samples about more tools, we can support more complicate plugins too.

## Major Improvement
- support langchain-MRKL(agent= "zero-shot-react-description")
- very fast because of stritcly format(it doesn't generate redundant tokens)