transformers safetensors qwen2 text-generation text-generation-inference Qwen conversational ta en dataset:prithivMLmods/Deepthink-Reasoning-Tamil base_model:Qwen/Qwen2.5-3B-Instruct base_model:finetune:Qwen/Qwen2.5-3B-Instruct license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model:
- Qwen/Qwen2.5-3B-Instruct
datasets:
- prithivMLmods/Deepthink-Reasoning-Tamil
language:
- ta
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
- Qwen
---
# **Qwen2.5-3B-Tamil-Exp**

**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.

### **Key Improvements**
1. **Advanced Reasoning & Logic:**  
   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.

2. **Fine-Tuned Instruction Following:**  
   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.

3. **Greater Adaptability:**  
   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.

4. **Long-Context Support:**  
   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.

5. **Multilingual Proficiency with Tamil Focus:**  
   While supporting over 20 languages, the model’s training emphasis on Tamil ensures superior performance on tasks involving Tamil language understanding and generation.

### **Intended Use**
- **Advanced Logical & Analytical Reasoning:**  
  Ideal for solving multi-step problems and deductive reasoning tasks, especially those presented in Tamil.
- **Mathematical & Scientific Computation:**  
  Supports theorem proving, complex calculations, and retrieval of scientific knowledge with an emphasis on Tamil terminology.
- **Code Generation & Debugging:**  
  Generates optimized code, detects errors, and enhances programming workflows with support for Tamil documentation or comments.
- **Structured Data Analysis:**  
  Processes tables, JSON, and other structured formats, which is particularly useful for localized applications requiring Tamil language outputs.
- **Multilingual Reasoning & Translation:**  
  While excelling in Tamil, it is also proficient in other languages for international applications.
- **Extended Text Generation:**  
  Capable of producing research papers, instructional guides, and in-depth reports in Tamil.

### **Quickstart with Transformers**

Below is an example of how to load and use the model with the Hugging Face Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your_org/Qwen2.5-3B-Tamil-Exp"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "தமிழில் தர்க்கரீதியான எண்ணத்தை விளக்குங்கள்."  # "Explain the concept of logical reasoning in Tamil."
messages = [
    {"role": "system", "content": "நீங்கள் ஒரு தமிழில் சிறந்த தர்க்கரீதியான உதவியாளர்."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

### **Limitations**
1. **Moderate Computational Requirements:**  
   Requires mid-end consumer GPUs for optimal inference.
2. **Language-Specific Variability:**  
   While performance is strong for Tamil, results may vary for other supported languages.
3. **Potential Error Accumulation:**  
   Extended outputs may sometimes introduce inconsistencies.
4. **Limited Real-World Awareness:**  
   The model’s knowledge is based on its training data and may not include recent events.
5. **Prompt Sensitivity:**  
   High-quality responses depend on the clarity and specificity of the input prompt.