Tamil-Llama: A Family of LLaMA-based LLMs focused on Tamil Language

Description

This repository contains the code and models for "Tamil-Llama", a project focused on enhancing the performance of language models for the Tamil language. It builds upon the open-source LLaMA model, introducing additional Tamil tokens and employing the LoRA methodology for efficient training. Please read the technical report for more details.

Technical Report: https://arxiv.org/abs/2311.05845

If you appreciate this work and would like to support its continued development, consider buying me a coffee. Your support is invaluable and greatly appreciated.

Updates

Feb 25, 2024

Google's Gemma 2B Model was adapter for Tamil (Experimental Release) based on the same framework with a few changes. More info in this LinkedIn post.

Note: I have migrated to Llama-Factory for pretraining and Axolotl for finetuning.

• No expansion in vocab for Gemma as it already has 256k vocab size and minnescule amounts of Tamil tokens.
• Continually pretrain on all available Tamil Wikipedia data for 3 epochs.
• Finetune on Tamil Alpaca + English Alpaca mix for 5 epochs
• Model tops Open LLM Leaderboard for models under 3B params as of Feb 2023.

Download Links:

• Tamil Gemma 2B Alpha
• Tamil Gemma 2B Alpha GGUF

Jan 23, 2024

For more details, please read the detailed blog post here.

• Tamil LLaMA v0.2 models are out. It is a significant upgrade compared to the earlier version.
  • Tamil LLaMA is now bilingual, it can fluently respond in both English and Tamil.
  • Better tokenizer.
  • Better base model.
  • Better fine tuning dataset and performance.
  • Our models match or betters the performance of Meta's LLaMA 2 is almost all the benchmarks.
• Following the same methodology the first ever Telugu and Malayam LLaMA models are also released.

Table of Contents

• Available Models
• Benchmark Scores
• Demo
• Getting Started
• Datasets
• Prompting Format
• Usage Note
• Contributions
• License
• Citation
• Contact

Available Models

Model	Type	Data	Base Model	# Params	Download Links
Tamil LLaMA 7B Base	Base model	12GB	LLaMA 7B	7B	HF Hub
Tamil LLaMA 13B Base	Base model	4GB	LLaMA 13B	13B	HF Hub
Tamil LLaMA 7B Instruct	Instruction following model	145k instructions	Tamil LLaMA 7B Base	7B	HF Hub
Tamil LLaMA 13B Instruct	Instruction following model	145k instructions	Tamil LLaMA 13B Base	13B	HF Hub

Quantized Version of Available Models

Model	Format	Bits	Download Links
Tamil LLaMA 7B Base	GGUF	Q4_K_M, Q5_K_M, Q8_0	HF Hub
Tamil LLaMA 13B Base	GGUF	Q4_K_M, Q5_K_M, Q8_0	HF Hub
Tamil LLaMA 7B Instruct	GGUF	Q4_K_M, Q5_K_M, Q8_0	HF Hub
Tamil LLaMA 13B Instruct	GGUF	Q4_K_M, Q5_K_M, Q8_0	HF Hub

Benchmark Scores

Scores are calculated using the HuggingFace Open LLM Leaderboard.

Note: The benchmarks test the model's capabilities in English reasoning, although the Tamil LLaMA models were not trained on quality reasoning tasks in English it shows decent performance across most benchmarks.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Tamil LLaMA 13B Instruct	51.59	54.52	79.35	50.37	41.22	76.56	7.51
Tamil LLaMA 13B Base	49.5	52.82	79.95	52.05	36.56	75.61	0
Tamil LLaMA 7B Instruct	45.52	48.04	70.97	39.95	41.7	70.64	1.82
Tamil LLaMA 7B Base	44.52	46.67	72.85	40.95	35.93	70.72	0

Demo

Update: There is now a Google Colab demo for Tamil/Telugu/Malayalam LLaMAs part of this project. Click Here to open the Colab Notebook.

A simple interactive demo of Tamil-LLaMA-7B-Instruct-v0.1 is hosted in the HuggingFace Space here -> abhinand/tamil-llama-playground

Getting Started

Using LMStudio:

LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.

1. Download and Install LM Studio: Begin by downloading LM Studio from the official website.

2. Locate the Tamil Llama Model: After installation, open LM Studio and use the search bar to find the "Tamil Llama" model. Alternatively, if you have the GGUF model ID, paste it directly into the search bar.

3. Download the Appropriate Model Variant: Depending on your system's specifications, select the appropriate variant of the Tamil Llama model. Click on the 'Download' button to start the download process.

4. Import the Preset JSON File: Once the model is downloaded, navigate to the 'Chat' tab in LM Studio. In the settings, find the 'Preset' menu and click on the dropdown. Select "Import Preset From File" and import the preset JSON file located at config/lm_studio/model_config.json in the repository.

5. Select and Load the Model: Click on "Select a model to load" located on the top bar. From the list, choose the Tamil Llama variant that you previously downloaded.

6. Initiate Conversations with the Model: The Tamil Llama model is now ready to use. You can start engaging in conversations in the chat area of LM Studio.

Using with Ollama:

1. Verify Ollama Installation: First, ensure that Ollama is correctly installed on your system. If not, install it from the official source.

2. Download the Modelfile: Access the GitHub repository and download the Modelfile. This file is necessary for setting up the Tamil Llama model in Ollama.

3. Prepare the Working Directory: Place the downloaded Modelfile and the model's GGUF file in the same directory. To work in this directory, use the cd command in your terminal to change to the appropriate directory.

4. Download the Tamil Llama Model: Execute the following command in your terminal to download the desired Tamil Llama model from the GitHub repository:

   curl -L https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1-gguf/resolve/main/tamil-llama-7b-v0.1-q8_0.gguf -o tamil-llama.gguf

   This command downloads the Tamil Llama model GGUF file and saves it as tamil-llama.gguf in your current directory.

5. Import and Run the Model in Ollama: After downloading the model, use the following command to create and run the Tamil Llama model in Ollama:

   ollama create tamil-llama -f Modelfile 

   This command imports the Tamil Llama model into Ollama and prepares it for use.

Optionally, depending upon your system's capabilities make sure to configure these parameters in the Modelfile too:

PARAMETER num_thread 8
PARAMETER num_gpu 0

For more information regarding the Modelfile's available parameters check out the official docs.

Datasets

The repository includes a Tamil-translated version of the Alpaca dataset and a subset of the OpenOrca dataset, which are used for instruction fine-tuning and evaluation.

Tamil Alpaca: abhinand/tamil-alpaca

Tamil Alpaca Orca: abhinand/tamil-alpaca-orca

Tamil LLaMA Eval: abhinand/tamil-llama-eval

Prompting Format for Instruction Models

Prompt Template Without Input

{system_prompt}

### Instruction:
{instruction or query}

### Response:
{response}

Prompt Template With Input

{system_prompt}

### Instruction:
{instruction or query}

### Input:
{input}

### Response:
{response}

Usage Note

It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.

Contributions

We welcome contributions to this project. If you have suggestions or improvements, please open an issue or a pull request.

License

This project is licensed under the GNU GPL v3.0 license - see the LICENSE.md file for details.

IMPORTANT: The GPL 3.0 License is applicable solely to the source code and datasets provided. As this project is a derivative of Meta's LLaMA 2 model, it is subject to the original licensing of LLaMA 2, which cannot be altered. Therefore, for comprehensive details regarding the licensing of the model, please consult the LLAMA2-LICENSE file.

Citation

If you use this model or the Tamil-Llama dataset in your research, please cite:

@misc{balachandran2023tamilllama,
      title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, 
      author={Abhinand Balachandran},
      year={2023},
      eprint={2311.05845},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contact

For any queries regarding the codebase or research, please reach out to Abhinand Balachandran at abhinandb.ml@gmail.com.