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Model: Tamil-ai/tamil-qwen25-7b-instruct Source: Original Platform
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README.md
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---
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language:
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- ta
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- tamil
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- qwen2
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- qlora
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- instruction-tuning
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- morphology
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- dravidian
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datasets:
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- Tamil-ai/samacheer-kalvi-tamil
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model-index:
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- name: Tamil-Qwen2.5-7B-Instruct
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results: []
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---
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# Tamil-Qwen2.5-7B-Instruct
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A Tamil-specialized instruction-tuned LLM built on Qwen2.5-7B-Instruct using QLoRA fine-tuning on 150K deduplicated Tamil instruction pairs.
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**Paper:** *"A Thousand Language Problem: Morphological Understanding in Linguistic AI"*
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## Model Details
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| Property | Value |
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|----------|-------|
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| Base model | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| Parameters | 7.6B |
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| Method | QLoRA (r=64, alpha=128, dropout=0.05) |
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| Training data | 150K deduplicated Tamil instruction-response pairs |
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| Tokenizer efficiency | 4.62x ratio (best among tested models for Tamil) |
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| Compute | RunPod RTX 5090, ~$5 total cost |
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| Sequence length | 1024 |
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| Batch size | 32 (effective) |
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| Epochs | 1 |
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## Training Data
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150,000 deduplicated instruction-response pairs from 5 Tamil datasets:
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- Tamil Alpaca
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- Tamil Orca
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- Tamil Dolly
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- Tamil-ai/samacheer-kalvi-tamil (morphological drills + grammar QA)
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- Additional Tamil instruction sets
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Tamil-ai/tamil-qwen25-7b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a helpful Tamil language assistant."},
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{"role": "user", "content": "வீடு என்ற சொல்லின் வேற்றுமை வடிவங்களைக் கூறுக."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### 4-bit Quantized (for limited VRAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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model = AutoModelForCausalLM.from_pretrained(
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"Tamil-ai/tamil-qwen25-7b-instruct",
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quantization_config=BitsAndBytesConfig(load_in_4bit=True),
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device_map="auto",
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)
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```
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## Why Qwen2.5?
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Tokenizer analysis across 6 base models showed Qwen2.5 has the best Tamil tokenization efficiency:
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| Model | Tamil Token Ratio | Verdict |
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|-------|------------------|---------|
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| **Qwen2.5** | **4.62x** | Best for Tamil |
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| Llama 3.1 | 5.8x | |
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| Gemma 2 | 6.1x | |
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| Mistral | 7.2x | |
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| Falcon | 10.5x | Worst |
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Lower ratio = fewer tokens per Tamil word = more efficient training and inference.
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## Intended Use
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- Tamil question answering and instruction following
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- Tamil morphological analysis
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- Tamil grammar and linguistics tasks
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- Research on low-resource language LLMs
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## Limitations
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- Trained primarily on instructional Tamil; may underperform on colloquial/slang
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- Morphological accuracy varies by category (see benchmark results)
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- English capabilities may degrade compared to base Qwen2.5
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## Citation
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```bibtex
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@misc{tamilai2026,
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title={A Thousand Language Problem: Morphological Understanding in Linguistic AI},
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author={Tamil-AI},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/Tamil-ai/tamil-qwen25-7b-instruct}
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}
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```
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config.json
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"torch_dtype": "bfloat16",
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"max_position_embeddings": 131072,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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"vocab_size": 152064
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3
tokenizer.json
Normal file
3
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version https://git-lfs.github.com/spec/v1
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15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
@@ -0,0 +1,15 @@
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||||
{
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||||
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}
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Reference in New Issue
Block a user