commit 06c728dab53738da9c3d3261c81394a93c2c34ff Author: ModelHub XC Date: Sat Jun 27 03:07:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Tamil-ai/tamil-qwen25-7b-instruct Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text 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100644 index 0000000..af10970 --- /dev/null +++ b/README.md @@ -0,0 +1,125 @@ +--- +language: +- ta +- en +license: apache-2.0 +base_model: Qwen/Qwen2.5-7B-Instruct +library_name: transformers +pipeline_tag: text-generation +tags: +- tamil +- qwen2 +- qlora +- instruction-tuning +- morphology +- dravidian +datasets: +- Tamil-ai/samacheer-kalvi-tamil +model-index: +- name: Tamil-Qwen2.5-7B-Instruct + results: [] +--- + +# 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](https://huggingface.co/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 + +```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)) +``` + +### 4-bit Quantized (for limited VRAM) + +```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", +) +``` + +## 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 + +```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} +} +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000..149a3fa --- /dev/null +++ b/config.json @@ -0,0 +1,63 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": null, + "torch_dtype": "bfloat16", + "eos_token_id": 151643, + "hidden_act": "silu", + "hidden_size": 3584, + 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