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Model: prithivMLmods/Kepler-Qwen3-4B-Super-Thinking-GGUF Source: Original Platform
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Kepler-Qwen3-4B-Super-Thinking.BF16.gguf
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Kepler-Qwen3-4B-Super-Thinking.F16.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q2_K.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q3_K_L.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q3_K_M.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q3_K_S.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q4_K_M.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q4_K_S.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q5_K_M.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q5_K_S.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q6_K.gguf
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size 3306260768
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Kepler-Qwen3-4B-Super-Thinking.Q8_0.gguf
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Kepler-Qwen3-4B-Super-Thinking.Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.F16.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Kepler-Qwen3-4B-Super-Thinking.BF16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- prithivMLmods/Kepler-Qwen3-4B-Super-Thinking
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- trl
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- Abliterated
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- Radiation
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---
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# **Kepler-Qwen3-4B-Super-Thinking-GGUF**
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> Kepler-Qwen3-4B-Super-Thinking is a reasoning-focused model fine-tuned on Qwen for Abliterated Reasoning and polished token probabilities, enhancing balanced multilingual generation across mathematics and general-purpose reasoning. It specializes in event-driven logic, structured analysis, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.
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## Model Files
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| File Name | Quant Type | File Size |
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| - | - | - |
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| Kepler-Qwen3-4B-Super-Thinking.BF16.gguf | BF16 | 8.05 GB |
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| Kepler-Qwen3-4B-Super-Thinking.F16.gguf | F16 | 8.05 GB |
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| Kepler-Qwen3-4B-Super-Thinking.F32.gguf | F32 | 16.1 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q2_K.gguf | Q2_K | 1.67 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q3_K_L.gguf | Q3_K_L | 2.24 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q3_K_M.gguf | Q3_K_M | 2.08 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q3_K_S.gguf | Q3_K_S | 1.89 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q4_K_M.gguf | Q4_K_M | 2.5 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q4_K_S.gguf | Q4_K_S | 2.38 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q5_K_M.gguf | Q5_K_M | 2.89 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q5_K_S.gguf | Q5_K_S | 2.82 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q6_K.gguf | Q6_K | 3.31 GB |
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| Kepler-Qwen3-4B-Super-Thinking.Q8_0.gguf | Q8_0 | 4.28 GB |
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## Quants Usage
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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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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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config.json
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config.json
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{
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"model_type": "qwen3"
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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