base_model: ajibawa-2023/Code-Llama-3-8B
datasets:
- ajibawa-2023/Code-290k-ShareGPT
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback
- microsoft/orca-math-word-problems-200k
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- code
- Python
- Cpp
- PHP
- JS
- Java
- Rust
- Ruby
- SQL
- MySql
- R
- Julia
About
weighted/imatrix quants of https://huggingface.co/ajibawa-2023/Code-Llama-3-8B
static quants are available at https://huggingface.co/mradermacher/Code-Llama-3-8B-GGUF
Usage
If 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.
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 | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| GGUF | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
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
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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.