base_model: cognitivecomputations/dolphincoder-starcoder2-15b
datasets:
- cognitivecomputations/dolphin
- jondurbin/airoboros-2.2.1
- cognitivecomputations/dolphin-coder
- teknium/openhermes
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- m-a-p/Code-Feedback
- m-a-p/CodeFeedback-Filtered-Instruction
language:
- en
library_name: transformers
license: bigcode-openrail-m
quantized_by: mradermacher
About
static quants of https://huggingface.co/cognitivecomputations/dolphincoder-starcoder2-15b
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphincoder-starcoder2-15b-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 | 6.3 | |
| GGUF | IQ3_XS | 6.8 | |
| GGUF | Q3_K_S | 7.1 | |
| GGUF | IQ3_S | 7.1 | beats Q3_K* |
| GGUF | IQ3_M | 7.4 | |
| GGUF | Q3_K_M | 8.1 | lower quality |
| GGUF | IQ4_XS | 8.8 | |
| GGUF | Q3_K_L | 9.1 | |
| GGUF | Q4_K_S | 9.3 | fast, recommended |
| GGUF | Q4_K_M | 10.0 | fast, recommended |
| GGUF | Q5_K_S | 11.1 | |
| GGUF | Q5_K_M | 11.5 | |
| GGUF | Q6_K | 13.2 | very good quality |
| GGUF | Q8_0 | 17.1 | fast, best 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.