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Model: mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF
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---
base_model: luzimu/FullStack-Learn-LM-30B-A3B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- code
- agent
---
## 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/luzimu/FullStack-Learn-LM-30B-A3B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#FullStack-Learn-LM-30B-A3B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-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/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.imatrix.gguf) | imatrix | 0.2 | imatrix file (for creating your own quants) |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ1_S.gguf) | i1-IQ1_S | 6.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ1_M.gguf) | i1-IQ1_M | 7.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ2_S.gguf) | i1-IQ2_S | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ2_M.gguf) | i1-IQ2_M | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 10.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q2_K.gguf) | i1-Q2_K | 11.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 11.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.7 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 13.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ3_S.gguf) | i1-IQ3_S | 13.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ3_M.gguf) | i1-IQ3_M | 13.6 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 14.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 16.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 16.5 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q4_0.gguf) | i1-Q4_0 | 17.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 17.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q4_1.gguf) | i1-Q4_1 | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/FullStack-Learn-LM-30B-A3B-i1-GGUF/resolve/main/FullStack-Learn-LM-30B-A3B.i1-Q6_K.gguf) | i1-Q6_K | 25.2 | 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. Additional thanks to [@nicoboss](https://huggingface.co/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.
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