---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Lacaille-MoT-4B-Supreme2
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
- code
- science
---

# **Lacaille-MoT-4B-Supreme2-GGUF**

> **Lacaille-MoT-4B-Supreme2** is a high-efficiency, multi-domain model fine-tuned on **Qwen3-4B** using the **Mixture of Thoughts (MoT)** dataset enhanced with **code, math, science expert clusters** and an extended **open code reasoning dataset**. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute.

## Model File Table

| File Name                                        | Size    | Format        | Description                              |
|--------------------------------------------------|---------|---------------|------------------------------------------|
| Lacaille-MoT-4B-Supreme2.BF16.gguf              | 8.05 GB | GGUF (BF16)   | BFloat16 precision model file            |
| Lacaille-MoT-4B-Supreme2.F16.gguf               | 8.05 GB | GGUF (F16)    | Float16 precision model file             |
| Lacaille-MoT-4B-Supreme2.F32.gguf               | 16.1 GB | GGUF (F32)    | Float32 precision model file             |
| Lacaille-MoT-4B-Supreme2.Q4_K_M.gguf            | 2.5 GB  | GGUF (Q4_K_M) | 4-bit quantized model file               |
| Lacaille-MoT-4B-Supreme2.Q5_K_M.gguf            | 2.89 GB | GGUF (Q5_K_M) | 5-bit quantized model file               |
| Lacaille-MoT-4B-Supreme2.Q8_0.gguf              | 4.28 GB | GGUF (Q8_0)   | 8-bit quantized model file               |
| config.json                                     | 31 B    | JSON          | Configuration file                       |
| .gitattributes                                  | 1.95 kB | Text          | Git attributes configuration             |

## 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)