language:
- en
- ta
license: other
datasets:
- vicgalle/alpaca-gpt4
- abhinand/tamil-alpaca
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: abhinand/gemma-2b-tamil
model-index:
- name: gemma-2b-it-tamil-v0.1-alpha
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: 50.09
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
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: 71.41
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
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: 39.94
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
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: 42.63
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
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: 64.96
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
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: 16.6
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-it-tamil-v0.1-alpha
name: Open LLM Leaderboard
Gemma 2B Tamil v0.1 Alpha [Experimental Release]
This is a Tamil instruction finetuned version of Google's Gemma 2B model. This is an experiment to see if Gemma can be adapted for Tamil without expanding vocabulary. While the responses may be rusty at times, it shows a lot of promise for a 2B parameter model.
Procedure:
- The Gemma base model was continually pretrained on all available Tamil Wikipedia data for 3 epochs.
- The updated model was then finetuned on a mix of English and Tamil alpaca datasets for 5 epochs.
Note: This project is currently under development (FOR TAMIL). The initial pretraining phase may not have been extensive enough, which suggests that the model's performance could improve by extending the pretraining on a larger dataset, such as CulturaX.
🏆 Benchmarks
This model outperforms Google's Gemma 2B base and instruct models on all benchmarks in Nous evaluation suite. It also surprisingly outperforms mlabonne/Gemmalpaca-2B (the best performing 2B model in benchmarks as of Feb 25, 2024) despite being a model aimed at language adaptation.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| gemma-2b-it-tamil-v0.1-alpha📄 | 39.41 | 23.38 | 58.94 | 43.18 | 32.14 |
| mlabonne/Gemmalpaca-2B 📄 | 38.39 | 24.48 | 51.22 | 47.02 | 30.85 |
| google/gemma-2b-it 📄 | 36.1 | 23.76 | 43.6 | 47.64 | 29.41 |
| google/gemma-2b 📄 | 34.26 | 22.7 | 43.35 | 39.96 | 31.03 |
Model description
- Model type: A 2B parameter GPT-like model finetuned on 100,000 samples consisting of an equal proportion of English and Tamil samples.
- Language(s): Bilingual. English and Tamil.
- License: Google Gemma Terms of Use
- Finetuned from model: abhinand/gemma-2b-tamil
- Training Precision:
bfloat16 - Training Hardware: 4x Nvidia RTX 3090 GPUs
- Training Cost: $20
Support my work
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.
Prompting Format [Alpaca]
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.
Meet the Developers
Get to know the creators behind this innovative model and follow their contributions to the field:
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. | 47.60 |
| AI2 Reasoning Challenge (25-Shot) | 50.09 |
| HellaSwag (10-Shot) | 71.41 |
| MMLU (5-Shot) | 39.94 |
| TruthfulQA (0-shot) | 42.63 |
| Winogrande (5-shot) | 64.96 |
| GSM8k (5-shot) | 16.60 |