---
base_model: win10/Qwen1.5-0.5b-Xia-Ai
datasets:
- c-s-ale/alpaca-gpt4-data-zh
- sahil2801/CodeAlpaca-20k
- TIGER-Lab/MathInstruct
language:
- en
- zh
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
license_name: tongyi-qianwen
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/win10/Qwen1.5-0.5b-Xia-Ai

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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q2_K.gguf) | Q2_K | 0.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q3_K_S.gguf) | Q3_K_S | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q3_K_L.gguf) | Q3_K_L | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.IQ4_XS.gguf) | IQ4_XS | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q4_K_S.gguf) | Q4_K_S | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q5_K_S.gguf) | Q5_K_S | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q5_K_M.gguf) | Q5_K_M | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q6_K.gguf) | Q6_K | 0.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-0.5b-Xia-Ai-GGUF/resolve/main/Qwen1.5-0.5b-Xia-Ai.f16.gguf) | f16 | 1.0 | 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)

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.

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