---
library_name: peft
base_model: openlm-research/open_llama_3b
---

# OpenLLaMa 3B PersonaChat
This is a LoRA finetune of [OpenLLaMa 3B](https://huggingface.co/openlm-research/open_llama_3b) on the [personachat-truecased](https://huggingface.co/datasets/bavard/personachat_truecased) dataset with 3 epochs of 500 steps.

## Use
Before using this model, you must first add these extra tokens:
```python
tokenizer.add_special_tokens({"additional_special_tokens": ["<|human|>", "<|bot|>", "<|endoftext|>"]})
model.resize_token_embeddings(len(tokenizer))
```
The model is finetuned with the format is as follows:
```
Personality:
 - [...]
 - [...]
<|human|>Hi there!<|endoftext|><|bot|>Hello!<|endoftext|>
```
To use this model, you must first define the personalities.
```python
personalities = """Personality:
 - [...]
 - [...]
"""
```
Then, follow the format:
```python
user = input(">>> ")
prompt = f"{personalities}<|human|>{user}<|endoftext|><|bot|>"
```

## Naming Format
[model name]-finetuned-[dataset]-e[number of epochs]-s[number of steps]

## 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.dev0