Update README.md

README.md

@@ -14,4 +14,90 @@ tags:
 - code
 - synlogic
 - moe
----
+---
+
+# **Megatron-Bots-1.7B-Reasoning**
+
+> **Megatron-Bots-1.7B-Reasoning** is a **logical reasoning and general-purpose thinking model** fine-tuned from **Qwen3-1.7B**, specifically designed for **advanced reasoning tasks and analytical problem-solving**. Built with data entries from the **SynLogic Dataset**, it excels at structured thinking, logical deduction, and comprehensive problem analysis in a compact yet powerful architecture.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Megatron-Bots-1.7B-Reasoning-GGUF](https://huggingface.co/prithivMLmods/Megatron-Bots-1.7B-Reasoning-GGUF)
+
+## **Key Features**
+1. **Advanced Logical Reasoning**  
+   Trained on the SynLogic Dataset to perform complex logical deductions, structured problem-solving, and analytical thinking across diverse domains with exceptional accuracy and clarity.
+
+2. **General-Purpose Thinking Engine**  
+   Capable of handling multi-step reasoning, causal analysis, pattern recognition, and systematic problem decomposition for a wide range of cognitive tasks.
+
+3. **Compact High-Performance Architecture**  
+   While only 1.7B parameters, this model delivers sophisticated reasoning capabilities with minimal resource requirements, making it ideal for deployment in resource-constrained environments.
+
+4. **SynLogic Dataset Foundation**  
+   Built upon carefully curated synthetic logic problems and reasoning patterns, ensuring robust performance across mathematical reasoning, logical puzzles, and analytical challenges.
+
+## **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Megatron-Bots-1.7B-Reasoning"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve this logic puzzle: If all A are B, and some B are C, what can we conclude about A and C?"
+
+messages = [
+    {"role": "system", "content": "You are an advanced reasoning assistant specialized in logical analysis and problem-solving."},
+    {"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,
+    temperature=0.1,  # Lower temperature for more consistent reasoning
+    do_sample=True
+)
+
+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**
+- **Educational Platforms**: Logical reasoning tutoring and step-by-step problem explanation for students.
+- **Research Applications**: Automated logical analysis and hypothesis generation for academic research.
+- **Decision Support Systems**: Structured analytical thinking for business and strategic decision-making.
+- **Puzzle and Game AI**: Advanced reasoning for complex puzzles, strategy games, and logical challenges.
+- **Code Analysis Tools**: Logical flow analysis and debugging assistance for software development.
+
+## **Limitations**
+1. **Reasoning Domain Specificity**:  
+   While strong in logical reasoning, performance may vary on tasks requiring extensive domain-specific knowledge outside the training scope.
+
+2. **SynLogic Dataset Constraints**:  
+   Training primarily on synthetic logic data may limit performance on real-world reasoning scenarios that require contextual understanding.
+
+3. **Parameter Scale Trade-offs**:  
+   The 1.7B parameter size, while efficient, may struggle with extremely complex multi-step reasoning chains compared to larger models.
+
+4. **Base Model Inheritance**:  
+   Inherits any limitations from Qwen3-1.7B's base architecture and potential biases from pretraining data.
+
+5. **Context Window Limitations**:  
+   May face challenges with very long reasoning chains that exceed the model's context window capacity.