tamil-qwen25-7b-instruct/README.md

language, license, base_model, library_name, pipeline_tag, tags, datasets, model-index

language	license	base_model	library_name	pipeline_tag	tags	datasets	model-index
ta en	apache-2.0	Qwen/Qwen2.5-7B-Instruct	transformers	text-generation	tamil qwen2 qlora instruction-tuning morphology dravidian	Tamil-ai/samacheer-kalvi-tamil	name results Tamil-Qwen2.5-7B-Instruct

Tamil-Qwen2.5-7B-Instruct

A Tamil-specialized instruction-tuned LLM built on Qwen2.5-7B-Instruct using QLoRA fine-tuning on 150K deduplicated Tamil instruction pairs.

Paper: "A Thousand Language Problem: Morphological Understanding in Linguistic AI"

Model Details

Property	Value
Base model	Qwen/Qwen2.5-7B-Instruct
Parameters	7.6B
Method	QLoRA (r=64, alpha=128, dropout=0.05)
Training data	150K deduplicated Tamil instruction-response pairs
Tokenizer efficiency	4.62x ratio (best among tested models for Tamil)
Compute	RunPod RTX 5090, ~$5 total cost
Sequence length	1024
Batch size	32 (effective)
Epochs	1

Training Data

150,000 deduplicated instruction-response pairs from 5 Tamil datasets:

• Tamil Alpaca
• Tamil Orca
• Tamil Dolly
• Tamil-ai/samacheer-kalvi-tamil (morphological drills + grammar QA)
• Additional Tamil instruction sets

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tamil-ai/tamil-qwen25-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful Tamil language assistant."},
    {"role": "user", "content": "வீடு என்ற சொல்லின் வேற்றுமை வடிவங்களைக் கூறுக."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

4-bit Quantized (for limited VRAM)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model = AutoModelForCausalLM.from_pretrained(
    "Tamil-ai/tamil-qwen25-7b-instruct",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
    device_map="auto",
)

Why Qwen2.5?

Tokenizer analysis across 6 base models showed Qwen2.5 has the best Tamil tokenization efficiency:

Model	Tamil Token Ratio	Verdict
Qwen2.5	4.62x	Best for Tamil
Llama 3.1	5.8x
Gemma 2	6.1x
Mistral	7.2x
Falcon	10.5x	Worst

Lower ratio = fewer tokens per Tamil word = more efficient training and inference.

Intended Use

• Tamil question answering and instruction following
• Tamil morphological analysis
• Tamil grammar and linguistics tasks
• Research on low-resource language LLMs

Limitations

• Trained primarily on instructional Tamil; may underperform on colloquial/slang
• Morphological accuracy varies by category (see benchmark results)
• English capabilities may degrade compared to base Qwen2.5

Citation

@misc{tamilai2026,
  title={A Thousand Language Problem: Morphological Understanding in Linguistic AI},
  author={Tamil-AI},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/Tamil-ai/tamil-qwen25-7b-instruct}
}