metadata

language:
  - ta
  - en
license: gpl-3.0

Tamil LLaMA 13B Instruct v0.1 [GGUF Quantized]

Welcome to the inaugural release of the Tamil LLaMA 13B instruct model – an important step in advancing LLMs for the Tamil language. This model is ready for immediate inference and is also primed for further fine-tuning to cater to your specific NLP tasks.

To dive deep into the development and capabilities of this model, please read the research paper and the introductory blog post (WIP) that outlines our journey and the model's potential impact.

Model description

The Tamil LLaMA models have been enhanced and tailored specifically with an extensive Tamil vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-2.

Model type: A 13B parameter GPT-like model fine-tuned on Tamil-Alpaca-Orca - a mix of Tamil-translated Stanford-Alpaca and a subset of OpenOrca datasets.
Language(s): Tamil and English
License: GNU General Public License v3.0
Finetuned from model: abhinand/tamil-llama-13b-base-v0.1
Training Precision: float16
Code: GitHub

Quantization Info

This repo contains GGUF format model files for Tamil LLaMA 13B Instruct v0.1.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
tamil-llama-13b-instruct-v0.1-q4_k_m.gguf	Q4_K_M	4	7.98 GB	10.5 GB	medium, balanced quality - recommended
tamil-llama-13b-instruct-v0.1-q5_k_m.gguf	Q5_K_M	5	9.35 GB	11.9 GB	large, very low quality loss - recommended
tamil-llama-13b-instruct-v0.1-q8_0.gguf	Q8_0	8	14.0 GB	16.33 GB	very large, extremely low quality loss - not recommended

Prompting Format

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}

Related 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

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.

Meet the Developers

Get to know the creators behind this innovative model and follow their contributions to the field:

Abhinand Balachandran

Citation

If you use this model or any of the the Tamil-Llama datasets 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}
}

We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.