language:
- ta
- en
license: llama2
model-index:
- name: tamil-llama-13b-base-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 52.82
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 79.95
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.05
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 36.56
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 75.61
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/tamil-llama-13b-base-v0.1
name: Open LLM Leaderboard
Tamil LLaMA 13B Base v0.1 [pre-trained]
Welcome to the inaugural release of the Tamil LLaMA 13B base 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.
Please Note: This model, labeled as a foundational Tamil Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Tamil, you may find abhinand/tamil-llama-13b-instruct-v0.1 more suitable for your needs.
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 model for Causal LM pre-trained on CulturaX dataset's Tamil subset.
- Language(s): Tamil and English
- License: GNU General Public License v3.0
- Source Model: meta-llama/Llama-2-13b-hf
- Training Precision:
float16 - Code: GitHub
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:
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.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 49.50 |
| AI2 Reasoning Challenge (25-Shot) | 52.82 |
| HellaSwag (10-Shot) | 79.95 |
| MMLU (5-Shot) | 52.05 |
| TruthfulQA (0-shot) | 36.56 |
| Winogrande (5-shot) | 75.61 |
| GSM8k (5-shot) | 0.00 |