mradermacher/AtomThink-LLaVA1.5-7B-GGUF overview

---

base_model: Kun-Xiang/AtomThink-LLaVA1.5-7B

datasets:

• Quinn777/AMATH-SFT

language:

• en

library_name: transformers

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

About

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static quants of https://huggingface.co/Kun-Xiang/AtomThink-LLaVA1.5-7B

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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/AtomThink-LLaVA1.5-7B-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 | 2.6 | |

| GGUF | Q3_K_S | 3.0 | |

| GGUF | Q3_K_M | 3.4 | lower quality |

| GGUF | Q3_K_L | 3.7 | |

| GGUF | IQ4_XS | 3.7 | |

| GGUF | Q4_K_S | 4.0 | fast, recommended |

| GGUF | Q4_K_M | 4.2 | fast, recommended |

| GGUF | Q5_K_S | 4.8 | |

| GGUF | Q5_K_M | 4.9 | |

| GGUF | Q6_K | 5.6 | very good quality |

| GGUF | Q8_0 | 7.3 | fast, best quality |

| GGUF | f16 | 13.6 | 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.

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