---
license: mit
library_name: peft
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---

**Website**: [FireAct Agent](https://fireact-agent.github.io)

# **FireAct Llama-2/CodeLlama**
FireAct Llama/CodeLlama is a collection of fine-tuned generative text models for performaning ReAct with external search tools. Links to other models can be found in the Index section.

## Foundation Model Details
*Note: As the foundation models, Llama-2 and CodeLlama, are developed by Meta, please also read the guidence and license on their website, [Llama-2](https://huggingface.co./meta-llama) and [CodeLlama](https://huggingface.co./codellama), before using FireAct models.*

**Model Developers** Sysmtem 2 Research, Cambridge LTL, Monash University, Princeton PLI.

**Variations** FireAct models including Llama-2-7B full fine-tuned models, and Llama-2-[7B,13B], CodeLlama-[7B,13B,34B] LoRA fine-tuned models. All released models are fine-tuned on multi-task (HotpotQA/StrategyQA/MMLU) and multi-types (ReAct/CoT/Reflexion) data. 

**Input** Models input text only.

**Output** Models generate text only.

## Index
**Full Fine-tuned Model**

FireAct Llama-2: 
- [fireact_llama_2_7b](https://huggingface.co./forestai/fireact_llama_2_7b) 

**LoRA Fine-tuned Model**

FireAct Llama-2: 
- [fireact_llama_2_7b_lora](https://huggingface.co./forestai/fireact_llama_2_7b_lora)
- [fireact_llama_2_13b_lora](https://huggingface.co./forestai/fireact_llama_2_13b_lora) 

FireAct CodeLlama: 
- [fireact_codellama_7b_lora](https://huggingface.co./forestai/fireact_codellama_7b_lora)
- [fireact_codellama_13b_lora](https://huggingface.co./forestai/fireact_codellama_13b_lora)
- [fireact_codellama_34b_lora](https://huggingface.co./forestai/fireact_codellama_34b_lora)


## LoRA Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions


- PEFT 0.4.0