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---
base_model: Delta-Vector/Hamanasu-15B-Adventure
datasets:
- NewEden/Orion-LIT
- NewEden/Orion-Asstr-Stories-16K
- Mielikki/Erebus-87k
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- Nitral-AI/ARES-ShareGPT
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k
- NewEden/Claude-Instruct-2.7K
- NewEden/Claude-Instruct-5K
- PocketDoc/Dans-Personamaxx-VN
- NewEden/vanilla-backrooms-claude-sharegpt
- PocketDoc/Dans-Prosemaxx-Cowriter-3-S
- NewEden/Misc-Mang-Sharegpt
- PocketDoc/Dans-Prosemaxx-Adventure
- PocketDoc/Dans-Failuremaxx-Adventure-3
- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot
- PocketDoc/Dans-Prosemaxx-InstructWriter-Continue
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- phi
- roleplay
- finetune
- storywriting
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Delta-Vector/Hamanasu-15B-Adventure
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-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-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ1_S.gguf) | i1-IQ1_S | 3.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ1_M.gguf) | i1-IQ1_M | 3.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ2_S.gguf) | i1-IQ2_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ2_M.gguf) | i1-IQ2_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q2_K.gguf) | i1-Q2_K | 5.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ3_S.gguf) | i1-IQ3_S | 6.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ3_M.gguf) | i1-IQ3_M | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q4_0.gguf) | i1-Q4_0 | 8.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q4_1.gguf) | i1-Q4_1 | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Hamanasu-15B-Adventure-i1-GGUF/resolve/main/Hamanasu-15B-Adventure.i1-Q6_K.gguf) | i1-Q6_K | 12.1 | 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 -->