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---
base_model: ajibawa-2023/Code-290k-6.7B-Instruct
datasets:
- ajibawa-2023/Code-290k-ShareGPT
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- code
---
## About
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static quants of https://huggingface.co/ajibawa-2023/Code-290k-6.7B-Instruct
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-i1-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/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Code-290k-6.7B-Instruct-GGUF/resolve/main/Code-290k-6.7B-Instruct.f16.gguf) | f16 | 13.6 | 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
## 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.
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