140 lines
6.7 KiB
Markdown
140 lines
6.7 KiB
Markdown
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---
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base_model: ValiantLabs/Qwen3-1.7B-ShiningValiant3
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datasets:
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- sequelbox/Celestia3-DeepSeek-R1-0528
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- sequelbox/Mitakihara-DeepSeek-R1-0528
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- sequelbox/Raiden-DeepSeek-R1
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language:
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- en
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library_name: transformers
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license: apache-2.0
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- shining-valiant
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- shining-valiant-3
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- valiant
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- valiant-labs
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- qwen
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- qwen-3
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- qwen-3-1.7b
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- 1.7b
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- reasoning
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- code
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- code-reasoning
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- science
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- science-reasoning
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- physics
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- biology
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- chemistry
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- earth-science
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- astronomy
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- machine-learning
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- artificial-intelligence
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- compsci
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- computer-science
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- information-theory
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- ML-Ops
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- math
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- cuda
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- deep-learning
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- transformers
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- agentic
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- LLM
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- neuromorphic
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- self-improvement
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- complex-systems
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- cognition
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- linguistics
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- philosophy
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- logic
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- epistemology
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- simulation
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- game-theory
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- knowledge-management
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- creativity
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- problem-solving
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- architect
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- engineer
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- developer
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- creative
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- analytical
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- expert
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- rationality
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- conversational
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- chat
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- instruct
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/ValiantLabs/Qwen3-1.7B-ShiningValiant3
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-1.7B-ShiningValiant3-i1-GGUF).***
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static quants are available at https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ1_S.gguf) | i1-IQ1_S | 0.6 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_S.gguf) | i1-IQ2_S | 0.8 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ2_M.gguf) | i1-IQ2_M | 0.8 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.8 | very low quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.9 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q2_K.gguf) | i1-Q2_K | 0.9 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.9 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_S.gguf) | i1-IQ3_S | 1.0 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.0 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.0 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.1 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.2 | prefer IQ4_XS |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q4_1.gguf) | i1-Q4_1 | 1.2 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.3 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-ShiningValiant3-i1-GGUF/resolve/main/Qwen3-1.7B-ShiningValiant3.i1-Q6_K.gguf) | i1-Q6_K | 1.5 | practically like static Q6_K |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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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.
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<!-- end -->
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