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For a convenient overview and download list, visit our model page for this model.
Modellquelle
Quellenbeschreibung
static quants of https://huggingface.co/Perciqa/Aurora-Code-1
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at
Quellen
1 QuelleVerifiziert 8. Aug.
Modellartefakte
11 ArtefakteQuellenauszüge
2 AuszügeIf you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
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.
Aurora-Code-1.Q3_K_L.gguf
gguf · 14,8 GB · SHA-256 5cd33cf73c06…8546 · Hugging Face
--- base_model: Perciqa/Aurora-Code-1 datasets: - perciqa/aurora-code-sft-v1 language: - en - fr - multilingual library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - code - coding - agentic - moe - code-generation - code-review - debugging - instruction-tuned - perciqa - aurora - canadian-ai - enterprise - lora - sft --- ## 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/Perciqa/Aurora-Code-1 <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Aurora-Code-1-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Aurora-Code-1-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](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q2_K.gguf) | Q2_K | 11.4 | | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q3_K_S.gguf) | Q3_K_S | 13.4 | | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q3_K_L.gguf) | Q3_K_L | 16.0 | | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.IQ4_XS.gguf) | IQ4_XS | 16.7 | | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-GGUF/resolve/main/Aurora-Code-1.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Aurora-Code-1-...
Source context: 1092 downloads · 0 likes · Library transformers · Repo mradermacher/Aurora-Code-1-GGUF
| IQ4_XS |
| 16.7 |
| GGUF | Q4_K_S | 17.6 | fast, recommended |
| GGUF | Q4_K_M | 18.7 | fast, recommended |
| GGUF | Q5_K_S | 21.2 |
| GGUF | Q5_K_M | 21.8 |
| GGUF | Q6_K | 25.2 | very good quality |
| GGUF | Q8_0 | 32.6 | fast, best quality |
