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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
---
# **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. |