Loading
¿Tienes un flujo de trabajo de ComfyUI? Obtén un Workflow Doc legible en tu navegador.Haz legible un flujo de trabajo.Pruébalo ahora ↗
For a convenient overview and download list, visit our model page for this model.
Fuente del modelo
Descripción de la fuente
weighted/imatrix quants of https://huggingface.co/EvoNet/EvoNet-8b-Reasoning
For a convenient overview and download list, visit our model page for this model.
static quants are available at
Fuentes
1 fuenteVerificado 20 oct
Artefactos del modelo
25 artefactosExtractos de fuentes
3 extractosIf 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)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | imatrix | 0.1 | imatrix file (for creating your own quants) |
| GGUF | i1-IQ1_S | 2.1 | for the desperate |
| GGUF | i1-IQ1_M | 2.3 | mostly desperate |
| GGUF | i1-IQ2_XXS | 2.5 | |
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. 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.
EvoNet-8b-Reasoning.i1-IQ2_XS.gguf
gguf · 2,43 GB · SHA-256 301784677d66…c065 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-IQ2_XXS.gguf
gguf · 2,23 GB · SHA-256 f9497f79f873…74d6 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-IQ3_XS.gguf
gguf · 3,28 GB · SHA-256 bada68f32edb…0da0 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-IQ3_XXS.gguf
gguf · 3,05 GB · SHA-256 20e75caddfb9…525d · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-IQ4_NL.gguf
gguf · 4,36 GB · SHA-256 bec22e5b40d0…b675 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-IQ4_XS.gguf
gguf · 4,14 GB · SHA-256 78e365a50f99…d1eb · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q2_K_S.gguf
gguf · 2,78 GB · SHA-256 b053b4ce6fa1…6e66 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q3_K_L.gguf
gguf · 4,03 GB · SHA-256 d7202f98f9d2…af7e · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q3_K_M.gguf
gguf · 3,74 GB · SHA-256 e8d274e99694…b77a · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q3_K_S.gguf
gguf · 3,41 GB · SHA-256 3459ad850fe8…3cb2 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q4_K_M.gguf
gguf · 4,58 GB · SHA-256 eb8d23a1e764…d842 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q4_K_S.gguf
gguf · 4,37 GB · SHA-256 87f2ecdebd61…da44 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q5_K_M.gguf
gguf · 5,34 GB · SHA-256 1221a2215533…9ee5 · Hugging Face
DescargarEvoNet-8b-Reasoning.i1-Q5_K_S.gguf
gguf · 5,21 GB · SHA-256 19d6fbead8e7…52c6 · Hugging Face
Descargar--- base_model: EvoNet/EvoNet-8b-Reasoning language: - vi - en library_name: transformers license: llama3.1 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - reasoning - cybersecurity - pentest - evonet - llama-3.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/EvoNet/EvoNet-8b-Reasoning <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#EvoNet-8b-Reasoning-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-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/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/resolve/main/EvoNet-8b-Reasoning.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/EvoNet-8b-Reasoning-i1-GGUF/r...
Source context: 311 downloads · 0 likes · Library transformers · Repo mradermacher/EvoNet-8b-Reasoning-i1-GGUF
Source context: 64 downloads · 0 likes · Library transformers · Repo mradermacher/EvoNet-8b-Reasoning-i1-GGUF
| i1-IQ2_XS |
| 2.7 |
| GGUF | i1-IQ2_S | 2.9 |
| GGUF | i1-IQ2_M | 3.0 |
| GGUF | i1-Q2_K_S | 3.1 | very low quality |
| GGUF | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 3.4 | lower quality |
| GGUF | i1-IQ3_XS | 3.6 |
| GGUF | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 3.8 | beats Q3_K* |
| GGUF | i1-IQ3_M | 3.9 |
| GGUF | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 4.5 |
| GGUF | i1-Q4_0 | 4.8 | fast, low quality |
| GGUF | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| GGUF | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 5.0 | fast, recommended |
| GGUF | i1-Q4_1 | 5.2 |
| GGUF | i1-Q5_K_S | 5.7 |
| GGUF | i1-Q5_K_M | 5.8 |
| GGUF | i1-Q6_K | 6.7 | practically like static Q6_K |
