mradermacher/GLM-OCR-GGUF overview

---

base_model: zai-org/GLM-OCR

language:

• zh
• en
• fr
• es
• ru
• de
• ja
• ko

library_name: transformers

license: mit

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• conversational

---

About

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static quants of https://huggingface.co/zai-org/GLM-OCR

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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/GLM-OCR-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 | 0.5 | |

| GGUF | Q3_K_S | 0.5 | |

| GGUF | Q3_K_M | 0.6 | lower quality |

| GGUF | Q3_K_L | 0.6 | |

| GGUF | mmproj-Q8_0 | 0.6 | multi-modal supplement |

| GGUF | IQ4_XS | 0.6 | |

| GGUF | Q4_K_S | 0.6 | fast, recommended |

| GGUF | Q4_K_M | 0.6 | fast, recommended |

| GGUF | Q5_K_S | 0.7 | |

| GGUF | Q5_K_M | 0.7 | |

| GGUF | Q6_K | 0.8 | very good quality |

| GGUF | mmproj-f16 | 1.0 | multi-modal supplement |

| GGUF | Q8_0 | 1.1 | fast, best quality |

| GGUF | f16 | 1.9 | 16 bpw, overkill |

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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