---
license: apache-2.0
datasets:
- MiniMaxAI/SynLogic
language:
- en
base_model:
- prithivMLmods/Qwen3-1.7B-ft-bf16
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- synlogic
- moe
- math
---

![09.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/pF-Cj8uXajLqKXwAvatZE.png)

# **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.