---
base_model: Delta-Vector/Hamanasu-QwQ-V1.5-Instruct
datasets:
- NewEden/Orion-LIT
- NewEden/Orion-Asstr-Stories-16K
- Mielikki/Erebus-87k
- NewEden/Hydrus-R1-Thinking-Sharegpt
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- Nitral-AI/ARES-ShareGPT
- NewEden/Hydrus-HelpSteer2
- PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations
- PocketDoc/Dans-Toolmaxx-Agent
- PocketDoc/Dans-Assistantmaxx-Tulu3-IF
- NewEden/Hydrus-SonnetOrca
- NewEden/Hydrus-Chat_error-Pure-Dove-sharegpt
- NewEden/No_Robots-R1-Filtered
- NewEden/GSM8K-R1-filtered
- NewEden/Hydrus_Anthropic_hh_harmful-sharegpt
- NewEden/Hydrus-Instruct-SmolTalk
- PocketDoc/Dans-Logicmaxx-Skunkworks
- PocketDoc/Dans-Logicmaxx-SAT-AP
- PocketDoc/Dans-Toolmaxx-ShellCommands
- PocketDoc/Dans-Taskmaxx-Edit
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- qwen
- roleplay
- finetune
- storywriting
---
## About

<!-- ### quantize_version: 2 -->
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static quants of https://huggingface.co/Delta-Vector/Hamanasu-QwQ-V1.5-Instruct

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-i1-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://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q2_K.gguf) | Q2_K | 12.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q3_K_S.gguf) | Q3_K_S | 14.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q3_K_L.gguf) | Q3_K_L | 17.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.IQ4_XS.gguf) | IQ4_XS | 18.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q5_K_S.gguf) | Q5_K_S | 22.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q5_K_M.gguf) | Q5_K_M | 23.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-QwQ-V1.5-Instruct-GGUF/resolve/main/Hamanasu-QwQ-V1.5-Instruct.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |

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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