--- base_model: DavidAU/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B datasets: - sequelbox/Celestia3-DeepSeek-R1-0528 - sequelbox/Mitakihara-DeepSeek-R1-0528 - sequelbox/Raiden-DeepSeek-R1 language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - merge - programming - code generation - code - coding - coder - chat - code - chat - qwen - qwen3 - qwencoder - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-2.4b - 2.4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - shining-valiant - shining-valiant-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-1.7b - 1.7b - 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 - float32 --- ## About weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-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-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.2 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q2_K.gguf) | i1-Q2_K | 1.2 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ3_S.gguf) | i1-IQ3_S | 1.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ3_M.gguf) | i1-IQ3_M | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.7 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q4_0.gguf) | i1-Q4_0 | 1.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q4_1.gguf) | i1-Q4_1 | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B-i1-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.7B.i1-Q6_K.gguf) | i1-Q6_K | 2.4 | 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.