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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Qwen/Qwen3-4B-Base |
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tags: |
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- llm |
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- indic |
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model-index: |
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- name: Hex-1 |
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results: [] |
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language: |
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- hi |
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- te |
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- ta |
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- ml |
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- kn |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Hex-1 |
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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. |
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## Model description |
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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. |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 7 |
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- total_train_batch_size: 56 |
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- total_eval_batch_size: 56 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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### Training results |
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### Performance Comparison on ARCC Dataset |
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| Benchmark | Gemma-2-2B | Llama-3.2-3B | Llama-3.1-8B | Sarvam-1 | Hex-1 | |
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|-----------|------------|--------------|---------------|-----------|--------| |
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| arcc_hi | 37.57 | 49.13 | 56.17 | 60.00 | 36.68 | |
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| arcc_ta | 32.78 | 34.70 | 44.78 | 57.04 | 38.65 | |
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| arcc_te | 30.00 | 34.09 | 43.04 | 59.39 | 37.96 | |
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| arcc_kn | 29.22 | 36.43 | 44.70 | 57.04 | 38.31 | |
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| arcc_ml | 29.91 | 33.22 | 46.78 | 58.96 | 29.60 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
