mradermacher/gemma-4-12B-coder-fable5-composer2.5-v1-bf16-GGUF overview

---

base_model: bingleai/gemma-4-12B-coder-fable5-composer2.5-v1-bf16

language:

• en

library_name: transformers

license: gemma

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• gemma4
• coding
• code
• reasoning
• thinking
• local-llm

---

About

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<!-- ### 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 -->

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static quants of https://huggingface.co/bingleai/gemma-4-12B-coder-fable5-composer2.5-v1-bf16

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For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-4-12B-coder-fable5-composer2.5-v1-bf16-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 | Q2_K | 4.9 | |

| GGUF | Q3_K_S | 5.6 | |

| GGUF | Q3_K_M | 6.2 | lower quality |

| GGUF | Q3_K_L | 6.7 | |

| GGUF | IQ4_XS | 6.8 | |

| GGUF | Q4_K_S | 7.1 | fast, recommended |

| GGUF | Q4_K_M | 7.5 | fast, recommended |

| GGUF | Q5_K_S | 8.4 | |

| GGUF | Q5_K_M | 8.6 | |

| GGUF | Q6_K | 9.9 | very good quality |

| GGUF | Q8_0 | 12.8 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.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, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @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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