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

tidelganesh/Qwen3-thirukkural-tamil-v2

Qwen3-thirukkural-tamil-v2

Training hyperparameters

Training results

Usage

Framework versions

tidelganesh/
Qwen3-thirukkural-tamil-v2