File size: 3,313 Bytes
65780c8 bceb96f 65780c8 a07cfd7 65780c8 6486f96 65780c8 cea16ec 6486f96 65780c8 dd29f22 65780c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | ---
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
should probably proofread and complete it, then remove this comment. -->
# 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 |