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metadata
base_model: cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k
datasets:
  - cognitivecomputations/Dolphin-2.9
  - teknium/OpenHermes-2.5
  - m-a-p/CodeFeedback-Filtered-Instruction
  - cognitivecomputations/dolphin-coder
  - cognitivecomputations/samantha-data
  - microsoft/orca-math-word-problems-200k
  - Locutusque/function-calling-chatml
  - internlm/Agent-FLAN
language:
  - en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
  - generated_from_trainer
  - axolotl

About

static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k

weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-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 12.9
GGUF IQ3_XS 14.3
GGUF Q3_K_S 15.1
GGUF IQ3_S 15.1 beats Q3_K*
GGUF IQ3_M 15.7
GGUF Q3_K_M 16.8 lower quality
GGUF Q3_K_L 18.2
GGUF IQ4_XS 18.7
GGUF Q4_K_S 19.7 fast, recommended
GGUF Q4_K_M 20.8 fast, recommended
GGUF Q5_K_S 23.8
GGUF Q5_K_M 24.4
GGUF Q6_K 28.3 very good quality
GGUF Q8_0 36.6 fast, best quality

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.