---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-4B-Base
tags:
- llm
- indic
model-index:
- name: Hex-1
  results: []
language:
- hi
- te
- ta
- ml
- kn
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Hex-1

This model is a fine-tuned version of [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on the alpaca_hindi, the alpaca_tamil, the alpaca_telugu and the alpaca_kannada datasets.

## Model description

Hex-1 is a 4-billion parameter language model specifically optimized for Indian languages. It supports five major Indian languages, including Hindi, Kannada, Telugu, Tamil and Malayalam. 
When benchmarked against leading models like Gemma-2B, LLaMA-3.2-3B, and Sarvam-1, Hex1 delivers best-in-class performance in all five supported languages on MMLU dataset. 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 56
- total_eval_batch_size: 56
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

### Performance Comparison on ARCC Dataset

| Benchmark | Gemma-2-2B | Llama-3.2-3B | Llama-3.1-8B | Sarvam-1 | Hex-1   |
|-----------|------------|--------------|---------------|-----------|--------|
| arcc_hi   | 37.57      | 49.13        | 56.17         | 60.00     | 36.68  |
| arcc_ta   | 32.78      | 34.70        | 44.78         | 57.04     | 38.65  |
| arcc_te   | 30.00      | 34.09        | 43.04         | 59.39     | 37.96  |
| arcc_kn   | 29.22      | 36.43        | 44.70         | 57.04     | 38.31  |
| arcc_ml   | 29.91      | 33.22        | 46.78         | 58.96     | 29.60  |



### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1