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--- |
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license: apache-2.0 |
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language: |
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- ta |
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- en |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- qwen |
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- deepseek |
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- text-generation-inference |
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--- |
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# **Reasoning-Distilled-ta-7B** |
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Reasoning-Distilled-ta-7B is based on the *Qwen [KT] model*, which was distilled by *DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B*. It has been fine-tuned on specialized datasets focusing on **Tamil language-based reasoning tasks** and chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving in the Tamil language, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks in Tamil. |
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# **Quickstart with Transformers** |
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Here is a code snippet using `apply_chat_template` to show you how to load the tokenizer and model and generate content in Tamil: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Reasoning-Distilled-ta-7B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "பெரிய மொழி மாதிரிகள் பற்றி ஒரு சிறிய அறிமுகத்தை தரவும்." |
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messages = [ |
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{"role": "system", "content": "நீங்கள் DeepSeek-AI மூலம் உருவாக்கப்பட்ட Reasoning-Distilled-ta-7B. நீங்கள் ஒரு சக்திவாய்ந்த தமிழ் பகுத்தறிவு உதவியாளர்."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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### **Intended Use:** |
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1. **Tamil Language Instruction-Following:** The model excels in understanding and executing detailed instructions in Tamil, making it ideal for automation systems, virtual assistants, and educational tools tailored for Tamil-speaking users. |
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2. **Tamil Text Generation:** It can produce coherent, logically structured, and contextually relevant text in Tamil for use in content creation, summarization, and report writing. |
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3. **Complex Reasoning Tasks in Tamil:** With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks in Tamil. |
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4. **Research and Development:** It can support researchers and developers in exploring advancements in Tamil language processing, logical reasoning, and fine-tuning methodologies. |
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5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving in Tamil by generating step-by-step solutions. |
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### **Limitations:** |
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1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains in Tamil. |
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2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data. |
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3. **Bias in Training Data:** The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts. |
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4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses. |
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5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments. |
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6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results. |
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7. **Limited Multilingual Support:** While optimized for Tamil, the model may not perform as well in other languages, especially those with significantly different linguistic structures. |
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This model is designed to empower Tamil-speaking users with advanced reasoning and text-generation capabilities, while also addressing the unique challenges of working with the Tamil language. |
