base_model: ibm-granite/granite-8b-code-instruct
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- code
- granite
About
static quants of https://huggingface.co/ibm-granite/granite-8b-code-instruct
weighted/imatrix quants are available at https://huggingface.co/mradermacher/granite-8b-code-instruct-i1-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 | Q2_K | 3.2 | |
| GGUF | IQ3_XS | 3.5 | |
| GGUF | Q3_K_S | 3.6 | |
| GGUF | IQ3_S | 3.7 | beats Q3_K* |
| GGUF | IQ3_M | 3.8 | |
| GGUF | Q3_K_M | 4.0 | lower quality |
| GGUF | Q3_K_L | 4.4 | |
| GGUF | IQ4_XS | 4.5 | |
| GGUF | Q4_K_S | 4.7 | fast, recommended |
| GGUF | Q4_K_M | 5.0 | fast, recommended |
| GGUF | Q5_K_S | 5.7 | |
| GGUF | Q5_K_M | 5.8 | |
| GGUF | Q6_K | 6.7 | very good quality |
| GGUF | Q8_0 | 8.7 | fast, best quality |
| 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
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.