metadata
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Open-Xi-Math-Preview
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
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 File
| File Name | Size | Commit Message | Time Uploaded |
|---|---|---|---|
.gitattributes |
2.39 kB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q2_K.gguf |
753 MB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q3_K_M.gguf |
924 MB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q4_K_M.gguf |
1.12 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q4_K_S.gguf |
1.07 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q5_K_M.gguf |
1.29 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q5_K_S.gguf |
1.26 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q6_K.gguf |
1.46 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.Q8_0.gguf |
1.89 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.BF16.gguf |
3.56 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.F16.gguf |
3.56 GB | Upload folder using huggingface_hub | About 2 hours ago |
Open-Xi-Math-Preview.F32.gguf |
7.11 GB | Upload folder using huggingface_hub | About 2 hours ago |
README.md |
578 Bytes | Update README.md | Less than a minute ago |
config.json |
31 Bytes | Upload folder using huggingface_hub | About 1 hour ago |
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 | Q2_K | 0.4 | |
| GGUF | Q3_K_S | 0.5 | |
| GGUF | Q3_K_M | 0.5 | lower quality |
| GGUF | Q3_K_L | 0.5 | |
| GGUF | IQ4_XS | 0.6 | |
| GGUF | Q4_K_S | 0.6 | fast, recommended |
| GGUF | Q4_K_M | 0.6 | fast, recommended |
| GGUF | Q5_K_S | 0.6 | |
| GGUF | Q5_K_M | 0.7 | |
| GGUF | Q6_K | 0.7 | very good quality |
| GGUF | Q8_0 | 0.9 | fast, best quality |
| GGUF | f16 | 1.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
