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---
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- ajibawa-2023/Python-Code-23k-ShareGPT
exported_from: Markhit/CodeLlama3-8B-Python
language:
- en
library_name: transformers
license: llama3
license_link: LICENSE
quantized_by: mradermacher
tags:
- code
---
## About
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static quants of https://huggingface.co/Markhit/CodeLlama3-8B-Python
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## 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/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/CodeLlama3-8B-Python-i1-GGUF/resolve/main/CodeLlama3-8B-Python.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## 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.
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