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---
base_model: raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: llama3
quantized_by: mradermacher
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- moe
- code
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-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/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ1_M.gguf) | i1-IQ1_M | 6.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q2_K.gguf) | i1-Q2_K | 9.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 11.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ3_S.gguf) | i1-IQ3_S | 11.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ3_M.gguf) | i1-IQ3_M | 11.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 12.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 13.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q4_0.gguf) | i1-Q4_0 | 14.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 14.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 15.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-i1-GGUF/resolve/main/Llama-3-Aplite-Instruct-4x8B.i1-Q6_K.gguf) | i1-Q6_K | 20.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->

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