mradermacher/qwen3-shining-valiant-instruct-fast-coder-reasoning-2.4b-gguf overview

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      "base_model": "DavidAU/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B",
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    "summary": "## About         static quants of https://huggingface.co/DavidAU/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B  ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-i1-GGUF",
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    "readme_markdown": "---\nbase_model: DavidAU/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B\ndatasets:\n- sequelbox/Celestia3-DeepSeek-R1-0528\n- sequelbox/Mitakihara-DeepSeek-R1-0528\n- sequelbox/Raiden-DeepSeek-R1\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n  readme_rev: 1\nquantized_by: mradermacher\ntags:\n- merge\n- programming\n- code generation\n- code\n- coding\n- coder\n- chat\n- code\n- chat\n- qwen\n- qwen3\n- qwencoder\n- esper\n- esper-3\n- valiant\n- valiant-labs\n- qwen\n- qwen-3\n- qwen-3-2.4b\n- 2.4b\n- reasoning\n- code\n- code-instruct\n- python\n- javascript\n- dev-ops\n- jenkins\n- terraform\n- scripting\n- powershell\n- azure\n- aws\n- gcp\n- cloud\n- problem-solving\n- architect\n- engineer\n- developer\n- creative\n- analytical\n- expert\n- rationality\n- conversational\n- chat\n- instruct\n- shining-valiant\n- shining-valiant-3\n- valiant\n- valiant-labs\n- qwen\n- qwen-3\n- qwen-3-1.7b\n- 1.7b\n- reasoning\n- code\n- code-reasoning\n- science\n- science-reasoning\n- physics\n- biology\n- chemistry\n- earth-science\n- astronomy\n- machine-learning\n- artificial-intelligence\n- compsci\n- computer-science\n- information-theory\n- ML-Ops\n- math\n- cuda\n- deep-learning\n- transformers\n- agentic\n- LLM\n- neuromorphic\n- self-improvement\n- complex-systems\n- cognition\n- linguistics\n- philosophy\n- logic\n- epistemology\n- simulation\n- game-theory\n- knowledge-management\n- creativity\n- problem-solving\n- architect\n- engineer\n- developer\n- creative\n- analytical\n- expert\n- rationality\n- conversational\n- chat\n- instruct\n- float32\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type:  -->\n<!-- ### tags:  -->\n<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->\n<!-- ### quants_skip:  -->\n<!-- ### skip_mmproj:  -->\nstatic quants of https://huggingface.co/DavidAU/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-i1-GGUF\n## Usage\n\nIf you are unsure how to use GGUF files, refer to one of [TheBloke's\nREADMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for\nmore details, including on how to concatenate multi-part files.\n\n## Provided Quants\n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\n| Link | Type | Size/GB | Notes |\n|:-----|:-----|--------:|:------|\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q2_K.gguf) | Q2_K | 1.1 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q3_K_S.gguf) | Q3_K_S | 1.3 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q3_K_M.gguf) | Q3_K_M | 1.4 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q3_K_L.gguf) | Q3_K_L | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.IQ4_XS.gguf) | IQ4_XS | 1.5 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q4_K_S.gguf) | Q4_K_S | 1.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q4_K_M.gguf) | Q4_K_M | 1.6 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q5_K_S.gguf) | Q5_K_S | 1.8 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q5_K_M.gguf) | Q5_K_M | 1.9 |  |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q6_K.gguf) | Q6_K | 2.1 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.Q8_0.gguf) | Q8_0 | 2.7 | fast, best quality |\n| [GGUF](https://huggingface.co/mradermacher/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B-GGUF/resolve/main/Qwen3-Shining-Valiant-Instruct-Fast-CODER-Reasoning-2.4B.f16.gguf) | f16 | 5.0 | 16 bpw, overkill |\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)\n\nAnd here are Artefact2's thoughts on the matter:\nhttps://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\n\n## FAQ / Model Request\n\nSee https://huggingface.co/mradermacher/model_requests for some answers to\nquestions you might have and/or if you want some other model quantized.\n\n## Thanks\n\nI thank my company, [nethype GmbH](https://www.nethype.de/), for letting\nme use its servers and providing upgrades to my workstation to enable\nthis work in my free time.\n\n<!-- end -->\n",
    "related_quantizations": []
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