---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- generated_from_trainer
model-index:
- name: tinyllama-lora-fast (Bangla Newspaper Model)
  results: []
datasets:
- 25Iqbal/BanglaNewspaperDataset
language:
- bn
- en
pipeline_tag: sentence-similarity
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tinyllama-lora-fast
Trained primarily on Prothom Alo news data, this model naturally writes in that newspaper’s concise, reportorial Bangla style.

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.
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
It achieves the following results on the evaluation set:
- Loss: 0.8744

## Model description


Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Adapter: PEFT-LoRA (r=16, alpha=32, dropout=0.05, bias=none)

Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Context length (train max): MAX_LEN=768

Optimization: AdamW (lr≈3e-5, warmup≈0.03, weight_decay≈0.05)

Batching: length-based bucketing + dynamic padding

Precision: fp16 inference-ready (training setup Kaggle/Colab-friendly)

Decoding (low hallucination preset): temperature=0.0, do_sample=False, no_repeat_ngram_size=4, repetition_penalty≈1.1–1.15

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0752        | 0.1111 | 200  | 1.0674          |
| 1.0248        | 0.2222 | 400  | 1.0167          |
| 0.9839        | 0.3333 | 600  | 0.9843          |
| 0.9479        | 0.4444 | 800  | 0.9620          |
| 0.9478        | 0.5556 | 1000 | 0.9457          |
| 0.914         | 0.6667 | 1200 | 0.9318          |
| 0.9329        | 0.7778 | 1400 | 0.9182          |
| 0.8929        | 0.8889 | 1600 | 0.9104          |
| 0.911         | 1.0    | 1800 | 0.9005          |
| 0.869         | 1.1111 | 2000 | 0.8949          |
| 0.8873        | 1.2222 | 2200 | 0.8892          |
| 0.8551        | 1.3333 | 2400 | 0.8848          |
| 0.8543        | 1.4444 | 2600 | 0.8812          |
| 0.8741        | 1.5556 | 2800 | 0.8779          |
| 0.8652        | 1.6667 | 3000 | 0.8760          |
| 0.8534        | 1.7778 | 3200 | 0.8751          |
| 0.8378        | 1.8889 | 3400 | 0.8745          |
| 0.8514        | 2.0    | 3600 | 0.8744          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.45.2
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.20.3