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metadata
base_model: ibm-granite/granite-20b-code-instruct-8k
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

weighted/imatrix quants of https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k

static quants are available at https://huggingface.co/mradermacher/granite-20b-code-instruct-8k-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-IQ1_S 4.6 for the desperate
GGUF i1-IQ1_M 5.0 mostly desperate
GGUF i1-IQ2_XXS 5.7
GGUF i1-IQ2_XS 6.3
GGUF i1-IQ2_S 6.6
GGUF i1-IQ2_M 7.2
GGUF i1-Q2_K 8.0 IQ3_XXS probably better
GGUF i1-IQ3_XXS 8.2 lower quality
GGUF i1-IQ3_XS 8.8
GGUF i1-IQ3_S 9.0 beats Q3_K*
GGUF i1-Q3_K_S 9.0 IQ3_XS probably better
GGUF i1-IQ3_M 9.7
GGUF i1-Q3_K_M 10.7 IQ3_S probably better
GGUF i1-IQ4_XS 11.0
GGUF i1-Q4_0 11.7 fast, low quality
GGUF i1-Q4_K_S 11.8 optimal size/speed/quality
GGUF i1-Q3_K_L 11.8 IQ3_M probably better
GGUF i1-Q4_K_M 12.9 fast, recommended
GGUF i1-Q5_K_S 14.1
GGUF i1-Q5_K_M 14.9
GGUF i1-Q6_K 16.7 practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @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.