---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Open-Xi-Math-Preview
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
---

# **Open-Xi-Math-Preview-GGUF**

> **Open-Xi-Math-Preview** is a **mathematics-focused reasoning model** fine-tuned on **Qwen2-1.5B-Instruct**, utilizing a **modular dataset** designed for enhancing **mathematical thinking**. It provides robust capabilities in symbolic reasoning, structured deduction, and compact coding — optimized for edge deployment on **resource-constrained devices**.

## Model Files

| File Name                          | Size    | Quantization | Format | Description                   |
| ---------------------------------- | ------- | ------------ | ------ | ----------------------------- |
| `Open-Xi-Math-Preview.BF16.gguf`   | 3.56 GB | BF16         | GGUF   | BFloat16 precision version    |
| `Open-Xi-Math-Preview.F16.gguf`    | 3.56 GB | FP16         | GGUF   | Float16 precision version     |
| `Open-Xi-Math-Preview.F32.gguf`    | 7.11 GB | FP32         | GGUF   | Float32 precision version     |
| `Open-Xi-Math-Preview.Q2_K.gguf`   | 753 MB  | Q2\_K        | GGUF   | 2-bit quantized (K variant)   |
| `Open-Xi-Math-Preview.Q3_K_M.gguf` | 924 MB  | Q3\_K\_M     | GGUF   | 3-bit quantized (K M variant) |
| `Open-Xi-Math-Preview.Q4_K_M.gguf` | 1.12 GB | Q4\_K\_M     | GGUF   | 4-bit quantized (K M variant) |
| `Open-Xi-Math-Preview.Q4_K_S.gguf` | 1.07 GB | Q4\_K\_S     | GGUF   | 4-bit quantized (K S variant) |
| `Open-Xi-Math-Preview.Q5_K_M.gguf` | 1.29 GB | Q5\_K\_M     | GGUF   | 5-bit quantized (K M variant) |
| `Open-Xi-Math-Preview.Q5_K_S.gguf` | 1.26 GB | Q5\_K\_S     | GGUF   | 5-bit quantized (K S variant) |
| `Open-Xi-Math-Preview.Q6_K.gguf`   | 1.46 GB | Q6\_K        | GGUF   | 6-bit quantized (K variant)   |
| `Open-Xi-Math-Preview.Q8_0.gguf`   | 1.89 GB | Q8\_0        | GGUF   | 8-bit quantized               |
| `.gitattributes`                   | 2.39 kB | —            | —      | Git LFS tracking file         |
| `config.json`                      | 31 B    | —            | —      | Configuration file            |
| `README.md`                        | 4.29 kB | —            | —      | Model documentation           |

## Quants Usage 

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

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) | Q2_K | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) | Q6_K | 0.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) | f16 | 1.6 | 16 bpw, overkill |

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)