Kanglish Shopping Queries: Fine-tuned to understand and process shopping-related queries in Kanglish (Kannada written in Roman script)
Multi-turn Conversations: Optimized for handling multi-turn dialogue and maintaining context across multiple exchanges
Kanglish to English Translation: Trained to translate from Kanglish to English
English to Kanglish Translation: Trained to translate from English to Kanglish

Fine-tuning Technique

The model uses the QLoRA (Quantized Low-Rank Adaptation) technique, which is an efficient fine-tuning approach that combines quantization with low-rank adaptation. This approach:

Reduces memory requirements significantly through quantization
Uses low-rank adaptation to update model weights efficiently
Enables fine-tuning of large models on consumer-grade GPUs
Maintains the base model's capabilities while adding task-specific knowledge

Quick start

python

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Akshaymp/ambari-7b-finetuned", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was fine-tuned using QLoRA (Quantized Low-Rank Adaptation) on the same datasets as mentioned in the Fine-tuning Details section.

Framework versions

TRL: 0.24.0
Transformers: 4.57.3
Pytorch: 2.9.1
Datasets: 4.3.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

bibtex

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}