GraySoft
Projects Models Compare Cloud benchmarks FAQ Download guIDE →
Model Intelligence Sheet

mradermacher/Latxa-Llama-3.1-70B-Instruct-v2-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: < quants: x f16 Q4 K S Q2 K Q8 0 Q6 K Q3 K M Q3 K S Q3 K L Q4…

transformersggufeuendataset:HiTZ/latxa-corpus-v2base_model:HiTZ/Latxa-Llama-3.1-70B-Instruct-v2base_model:quantized:HiTZ/Latxa-Llama-3.1-70B-Instruct-v2license:otherendpoints_compatibleregion:usconversational

Runs locally from ~24.56 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).

Downloads
183
Likes
0
Pipeline

Repository Files & Downloads

11 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Latxa-Llama-3.1-70B-Instruct-v2.IQ4_XS.ggufGGUFGGUF35.64 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q2_K.ggufGGUFGGUF24.56 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q3_K_L.ggufGGUFGGUF34.59 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q3_K_M.ggufGGUFGGUF31.91 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q3_K_S.ggufGGUFGGUF28.79 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q4_K_M.ggufGGUFGGUF39.60 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q4_K_S.ggufGGUFGGUF37.58 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q5_K_M.ggufGGUFGGUF46.52 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q5_K_S.ggufGGUFGGUF45.32 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q6_K.ggufGGUFGGUF53.91 GBDownload
Latxa-Llama-3.1-70B-Instruct-v2.Q8_0.ggufGGUFGGUF69.83 GBDownload

Model Details

Model IDmradermacher/Latxa-Llama-3.1-70B-Instruct-v2-GGUF
Authormradermacher
Pipeline
Licenseother
Base modelHiTZ/Latxa-Llama-3.1-70B-Instruct-v2
Last modified2026-07-27T05:00:48.000Z

Model README

---

base_model: HiTZ/Latxa-Llama-3.1-70B-Instruct-v2

datasets:

  • HiTZ/latxa-corpus-v2

language:

  • eu
  • en

library_name: transformers

license: other

license_link: https://huggingface.co/HiTZ/Latxa-Llama-3.1-8B-Instruct/blob/main/LICENSE

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: -->

<!-- ### 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 -->

<!-- ### quants_skip: -->

<!-- ### skip_mmproj: -->

static quants of https://huggingface.co/HiTZ/Latxa-Llama-3.1-70B-Instruct-v2

<!-- provided-files -->

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Latxa-Llama-3.1-70B-Instruct-v2-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 | 26.5 | |

| GGUF | Q3_K_S | 31.0 | |

| GGUF | Q3_K_M | 34.4 | lower quality |

| GGUF | Q3_K_L | 37.2 | |

| GGUF | IQ4_XS | 38.4 | |

| GGUF | Q4_K_S | 40.4 | fast, recommended |

| GGUF | Q4_K_M | 42.6 | fast, recommended |

| GGUF | Q5_K_S | 48.8 | |

| GGUF | Q5_K_M | 50.0 | |

| GGUF | Q6_K | 58.0 | very good quality |

| GGUF | Q8_0 | 75.1 | 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.

<!-- end -->

Run mradermacher/Latxa-Llama-3.1-70B-Instruct-v2-GGUF with guIDE

Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.

Download guIDE → · Browse 524k+ models · Compare models

Source: Hugging Face · Compare models