mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF
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Model: mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF
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---
base_model: xsanskarx/qwen2-0.5b_numina_math-instruct
datasets:
- AI-MO/NuminaMath-CoT
language:
- en
library_name: transformers
license: cc-by-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- chat
- qwen2
- conversational
- math
- maths
- unsloth
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/xsanskarx/qwen2-0.5b_numina_math-instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qwen2-0.5b_numina_math-instruct-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/qwen2-0.5b_numina_math-instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://www.modelscope.cn/models/mradermacher/qwen2-0.5b_numina_math-instruct-i1-GGUF/resolve/master/qwen2-0.5b_numina_math-instruct.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
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)
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->