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Tamil-ai
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Model Details
Model Provider
Tamil-ai
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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 |
150,000 deduplicated instruction-response pairs from 5 Tamil datasets:
python
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))python
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",)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.
bibtex
@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}}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))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",)@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}}