Update README.md

README.md

@@ -11,4 +11,65 @@ tags:
 - qwen
 - deepseek
 - text-generation-inference
----
+---
+# **Reasoning-Distilled-ta-7B**  
+
+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.  
+
+# **Quickstart with Transformers**  
+
+Here is a code snippet using `apply_chat_template` to show you how to load the tokenizer and model and generate content in Tamil:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Reasoning-Distilled-ta-7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "பெரிய மொழி மாதிரிகள் பற்றி ஒரு சிறிய அறிமுகத்தை தரவும்."
+messages = [
+    {"role": "system", "content": "நீங்கள் DeepSeek-AI மூலம் உருவாக்கப்பட்ட Reasoning-Distilled-ta-7B. நீங்கள் ஒரு சக்திவாய்ந்த தமிழ் பகுத்தறிவு 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=512
+)
+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)
+```
+
+### **Intended Use:**  
+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.  
+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.  
+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.  
+4. **Research and Development:** It can support researchers and developers in exploring advancements in Tamil language processing, logical reasoning, and fine-tuning methodologies.  
+5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving in Tamil by generating step-by-step solutions.  
+
+### **Limitations:**  
+1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains in Tamil.  
+2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.  
+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.  
+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.  
+5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.  
+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.  
+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.  
+
+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.