Files
Qwen3-thirukkural-tamil-v2/README.md
ModelHub XC 4f58b2f5d2 初始化项目,由ModelHub XC社区提供模型
Model: tidelganesh/Qwen3-thirukkural-tamil-v2
Source: Original Platform
2026-09-21 14:26:16 +08:00

73 lines
2.4 KiB
Markdown

---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
- generated_from_trainer
model-index:
- name: Qwen3-thirukkural-tamil-v2
results: []
---
<!-- 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. -->
# Qwen3-thirukkural-tamil-v2
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).
It achieves the following results on the evaluation set:
- Loss: 0.2986
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2331 | 1.0 | 200 | 0.3307 |
| 0.1840 | 2.0 | 400 | 0.3093 |
| 0.1735 | 3.0 | 600 | 0.3013 |
| 0.1550 | 4.0 | 800 | 0.2983 |
| 0.1789 | 5.0 | 1000 | 0.2980 |
| 0.1689 | 6.0 | 1200 | 0.2986 |
### Usage
``` Python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tidelganesh/Qwen3-thirukkural-tamil" # your repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto")
messages = [{"role": "user", "content": "பொறையுடைமை அதிகாரத்தில் வரும் 158ஆம் குறளைத் தருக."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
**inputs, max_new_tokens=150,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
### Framework versions
- Transformers 5.14.1
- Pytorch 2.8.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2