metadata

license: mit
tags:
  - peft
  - lora
  - tinyllama
  - dialogue summarization
  - text2text-generation
datasets:
  - dialogsum
metrics:
  - rouge

TinyLlama Dialogue Summarization Fine-Tuned Model

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 specifically for dialogue summarization tasks. It was fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with LoRA on the DialogSum dataset.

Model Details

Model Name: TinyLlama-1.1B-Chat-v1.0 (fine-tuned)
Architecture: Causal Language Model
Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) with LoRA
Dataset: DialogSum
Quantization: 4-bit quantization was used during training to reduce memory consumption.

Intended Uses

This model is intended for generating concise and accurate summaries of dialogues. It can be used for various applications, including:

Summarizing customer service conversations.
Generating meeting summaries.
Creating summaries of chat logs.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
peft_model_id = "artisokka/tinyllama-dialogsum-finetuned"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, peft_model_id)

input_text = "Dialogue: ... (your dialogue here) ..."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)

outputs = model.generate(input_ids, max_length=200)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(summary)