README.md

---
base_model: ValiantLabs/Qwen3-8B-ShiningValiant3
datasets:
- sequelbox/Celestia3-DeepSeek-R1-0528
- sequelbox/Mitakihara-DeepSeek-R1-0528
- sequelbox/Raiden-DeepSeek-R1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- shining-valiant
- shining-valiant-3
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-8b
- 8b
- reasoning
- code
- code-reasoning
- science
- science-reasoning
- physics
- biology
- chemistry
- earth-science
- astronomy
- machine-learning
- artificial-intelligence
- compsci
- computer-science
- information-theory
- ML-Ops
- math
- cuda
- deep-learning
- transformers
- agentic
- LLM
- neuromorphic
- self-improvement
- complex-systems
- cognition
- linguistics
- philosophy
- logic
- epistemology
- simulation
- game-theory
- knowledge-management
- creativity
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
---
## About

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static quants of https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-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/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q2_K.gguf) | Q2_K | 3.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q3_K_S.gguf) | Q3_K_S | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q3_K_L.gguf) | Q3_K_L | 4.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.IQ4_XS.gguf) | IQ4_XS | 4.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q5_K_S.gguf) | Q5_K_S | 5.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q5_K_M.gguf) | Q5_K_M | 6.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-ShiningValiant3-GGUF/resolve/main/Qwen3-8B-ShiningValiant3.f16.gguf) | f16 | 16.5 | 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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