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open-llama-3b-finetuned-persona-e3-s500

Name: open-llama-3b-finetuned-persona-e3-s500
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OpenLLaMa 3B PersonaChat

This is a LoRA finetune of OpenLLaMa 3B on the personachat-truecased dataset with 3 epochs of 500 steps.

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|>"

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

The following bitsandbytes quantization config was used during training: