Instructions to use tidelganesh/Qwen3-thirukkural-tamil-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tidelganesh/Qwen3-thirukkural-tamil-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tidelganesh/Qwen3-thirukkural-tamil-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tidelganesh/Qwen3-thirukkural-tamil-v2")
model = AutoModelForCausalLM.from_pretrained("tidelganesh/Qwen3-thirukkural-tamil-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tidelganesh/Qwen3-thirukkural-tamil-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tidelganesh/Qwen3-thirukkural-tamil-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tidelganesh/Qwen3-thirukkural-tamil-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tidelganesh/Qwen3-thirukkural-tamil-v2

SGLang

How to use tidelganesh/Qwen3-thirukkural-tamil-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tidelganesh/Qwen3-thirukkural-tamil-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tidelganesh/Qwen3-thirukkural-tamil-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tidelganesh/Qwen3-thirukkural-tamil-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tidelganesh/Qwen3-thirukkural-tamil-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tidelganesh/Qwen3-thirukkural-tamil-v2 with Docker Model Runner:
```
docker model run hf.co/tidelganesh/Qwen3-thirukkural-tamil-v2
```

Qwen3-thirukkural-tamil-v2

This model is a fine-tuned version of Qwen/Qwen3-0.6B on an Thirukkural dataset. 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


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

Downloads last month: 85

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for tidelganesh/Qwen3-thirukkural-tamil-v2

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1344)

this model