86 lines
2.5 KiB
Markdown
86 lines
2.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen3-0.6B
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tags:
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- generated_from_trainer
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model-index:
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- name: Qwen3-thirukkural-tamil
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results: []
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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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# Qwen3-thirukkural-tamil
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This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) on an [Thirukkural dataset](https://huggingface.co/datasets/aitamilnadu/thirukkural_instruct)
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It achieves the following results on the evaluation set:
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- Loss: 0.2378
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.2613 | 1.0 | 200 | 0.2644 |
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| 0.2322 | 2.0 | 400 | 0.2482 |
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| 0.2235 | 3.0 | 600 | 0.2403 |
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| 0.2022 | 4.0 | 800 | 0.2380 |
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| 0.1972 | 5.0 | 1000 | 0.2374 |
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| 0.2086 | 6.0 | 1200 | 0.2378 |
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### Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tidelganesh/Qwen3-thirukkural-tamil" # update to your actual repo name
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto")
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messages = [
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{"role": "user", "content": "பொறையுடைமை அதிகாரத்தில் வரும் 158ஆம் குறளைத் தருக."}
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]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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output = model.generate(
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**inputs,
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max_new_tokens=150,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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output[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True,
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)
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print(response)
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```
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### Framework versions
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- Transformers 5.14.1
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- Pytorch 2.8.0+cu128
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- Datasets 5.0.0
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- Tokenizers 0.22.2
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