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--- |
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library_name: peft |
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license: apache-2.0 |
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: tinyllama-lora-fast (Bangla Newspaper Model) |
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results: [] |
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datasets: |
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- 25Iqbal/BanglaNewspaperDataset |
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language: |
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- bn |
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- en |
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pipeline_tag: sentence-similarity |
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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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# tinyllama-lora-fast |
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Trained primarily on Prothom Alo news data, this model naturally writes in that newspaper’s concise, reportorial Bangla style. |
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) Bangla Newspaper Dataset. |
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The single goal: enable fast training and low VRAM usage—even on small GPUs or TPUs.Use Dynamic Padding + Token Budget for low Gpu And Tpu |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8744 |
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## Model description |
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Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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Adapter: PEFT-LoRA (r=16, alpha=32, dropout=0.05, bias=none) |
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Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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Context length (train max): MAX_LEN=768 |
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Optimization: AdamW (lr≈3e-5, warmup≈0.03, weight_decay≈0.05) |
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Batching: length-based bucketing + dynamic padding |
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Precision: fp16 inference-ready (training setup Kaggle/Colab-friendly) |
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Decoding (low hallucination preset): temperature=0.0, do_sample=False, no_repeat_ngram_size=4, repetition_penalty≈1.1–1.15 |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.0752 | 0.1111 | 200 | 1.0674 | |
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| 1.0248 | 0.2222 | 400 | 1.0167 | |
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| 0.9839 | 0.3333 | 600 | 0.9843 | |
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| 0.9479 | 0.4444 | 800 | 0.9620 | |
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| 0.9478 | 0.5556 | 1000 | 0.9457 | |
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| 0.914 | 0.6667 | 1200 | 0.9318 | |
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| 0.9329 | 0.7778 | 1400 | 0.9182 | |
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| 0.8929 | 0.8889 | 1600 | 0.9104 | |
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| 0.911 | 1.0 | 1800 | 0.9005 | |
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| 0.869 | 1.1111 | 2000 | 0.8949 | |
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| 0.8873 | 1.2222 | 2200 | 0.8892 | |
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| 0.8551 | 1.3333 | 2400 | 0.8848 | |
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| 0.8543 | 1.4444 | 2600 | 0.8812 | |
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| 0.8741 | 1.5556 | 2800 | 0.8779 | |
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| 0.8652 | 1.6667 | 3000 | 0.8760 | |
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| 0.8534 | 1.7778 | 3200 | 0.8751 | |
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| 0.8378 | 1.8889 | 3400 | 0.8745 | |
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| 0.8514 | 2.0 | 3600 | 0.8744 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.45.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.1.1 |
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- Tokenizers 0.20.3 |
