---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Megatron-Bots-1.7B-Reasoning
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---

# **Megatron-Bots-1.7B-Reasoning-GGUF**

> **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.


## Model Files

| File Name | Size | Format | Description |
|-----------|------|--------|-------------|
| Megatron-Bots-1.7B-Reasoning.F32.gguf | 6.89 GB | F32 | Full precision 32-bit floating point |
| Megatron-Bots-1.7B-Reasoning.F16.gguf | 3.45 GB | F16 | Half precision 16-bit floating point |
| Megatron-Bots-1.7B-Reasoning.BF16.gguf | 3.45 GB | BF16 | Brain floating point 16-bit |
| Megatron-Bots-1.7B-Reasoning.Q8_0.gguf | 1.83 GB | Q8_0 | 8-bit quantized |
| Megatron-Bots-1.7B-Reasoning.Q6_K.gguf | 1.42 GB | Q6_K | 6-bit quantized |
| Megatron-Bots-1.7B-Reasoning.Q5_K_M.gguf | 1.26 GB | Q5_K_M | 5-bit quantized, medium quality |
| Megatron-Bots-1.7B-Reasoning.Q5_K_S.gguf | 1.23 GB | Q5_K_S | 5-bit quantized, small quality |
| Megatron-Bots-1.7B-Reasoning.Q4_K_M.gguf | 1.11 GB | Q4_K_M | 4-bit quantized, medium quality |
| Megatron-Bots-1.7B-Reasoning.Q4_K_S.gguf | 1.06 GB | Q4_K_S | 4-bit quantized, small quality |
| Megatron-Bots-1.7B-Reasoning.Q3_K_L.gguf | 1 GB | Q3_K_L | 3-bit quantized, large quality |
| Megatron-Bots-1.7B-Reasoning.Q3_K_M.gguf | 940 MB | Q3_K_M | 3-bit quantized, medium quality |
| Megatron-Bots-1.7B-Reasoning.Q3_K_S.gguf | 867 MB | Q3_K_S | 3-bit quantized, small quality |
| Megatron-Bots-1.7B-Reasoning.Q2_K.gguf | 778 MB | Q2_K | 2-bit quantized |

## Quants Usage 

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)