100 lines
4.8 KiB
Markdown
100 lines
4.8 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- prithivMLmods/Deepthink-Reasoning-Tamil
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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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- Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- Qwen
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---
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# **Qwen2.5-3B-Tamil-Exp**
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**Qwen2.5-3B-Tamil-Exp** is built on the robust Qwen2.5 architecture and has been specifically adapted to excel at Tamil language tasks. By incorporating training log entries from the prithivMLmods/Deepthink-Reasoning-Tamil dataset along with the proven reasoning framework of Qwen models, this 3B-parameter variant achieves enhanced chain-of-thought reasoning and logical problem solving—especially tailored for Tamil. Its improvements extend to context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation in Tamil and other languages.
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### **Key Improvements**
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1. **Advanced Reasoning & Logic:**
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Optimized for multi-step problem solving and logical deduction. Fine-tuning on the Deepthink-Reasoning-Tamil entries further refines its reasoning capabilities in Tamil contexts.
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2. **Fine-Tuned Instruction Following:**
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Generates precise responses and structured outputs (such as JSON), making it well-suited for dialog-based applications and code generation tasks that require strict adherence to Tamil language instructions.
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3. **Greater Adaptability:**
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Excels in role-playing scenarios, multi-turn dialogues, and diverse system prompts with a focus on culturally nuanced Tamil content while maintaining support for multiple languages.
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4. **Long-Context Support:**
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Capable of handling extended inputs (up to 64K tokens) and generating outputs of up to 4K tokens, enabling the processing of detailed and lengthy Tamil texts.
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5. **Multilingual Proficiency with Tamil Focus:**
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While supporting over 20 languages, the model’s training emphasis on Tamil ensures superior performance on tasks involving Tamil language understanding and generation.
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### **Intended Use**
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- **Advanced Logical & Analytical Reasoning:**
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Ideal for solving multi-step problems and deductive reasoning tasks, especially those presented in Tamil.
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- **Mathematical & Scientific Computation:**
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Supports theorem proving, complex calculations, and retrieval of scientific knowledge with an emphasis on Tamil terminology.
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- **Code Generation & Debugging:**
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Generates optimized code, detects errors, and enhances programming workflows with support for Tamil documentation or comments.
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- **Structured Data Analysis:**
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Processes tables, JSON, and other structured formats, which is particularly useful for localized applications requiring Tamil language outputs.
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- **Multilingual Reasoning & Translation:**
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While excelling in Tamil, it is also proficient in other languages for international applications.
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- **Extended Text Generation:**
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Capable of producing research papers, instructional guides, and in-depth reports in Tamil.
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### **Quickstart with Transformers**
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Below is an example of how to load and use the model with the Hugging Face Transformers library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "your_org/Qwen2.5-3B-Tamil-Exp"
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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 = "தமிழில் தர்க்கரீதியான எண்ணத்தை விளக்குங்கள்." # "Explain the concept of logical reasoning in Tamil."
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messages = [
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{"role": "system", "content": "நீங்கள் ஒரு தமிழில் சிறந்த தர்க்கரீதியான 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=256
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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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### **Limitations**
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1. **Moderate Computational Requirements:**
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Requires mid-end consumer GPUs for optimal inference.
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2. **Language-Specific Variability:**
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While performance is strong for Tamil, results may vary for other supported languages.
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3. **Potential Error Accumulation:**
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Extended outputs may sometimes introduce inconsistencies.
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4. **Limited Real-World Awareness:**
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The model’s knowledge is based on its training data and may not include recent events.
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5. **Prompt Sensitivity:**
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High-quality responses depend on the clarity and specificity of the input prompt.
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