base_model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
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: llama3
quantized_by: mradermacher
tags:
- generated_from_trainer
- axolotl
About
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.1-llama-3-70b
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.1-llama-3-70b-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 | 26.5 | |
| GGUF | IQ3_XS | 29.4 | |
| GGUF | IQ3_S | 31.0 | beats Q3_K* |
| GGUF | Q3_K_S | 31.0 | |
| GGUF | IQ3_M | 32.0 | |
| GGUF | Q3_K_M | 34.4 | lower quality |
| GGUF | Q3_K_L | 37.2 | |
| GGUF | IQ4_XS | 38.4 | |
| GGUF | Q4_K_S | 40.4 | fast, recommended |
| GGUF | Q4_K_M | 42.6 | fast, recommended |
| GGUF | Q5_K_S | 48.8 | |
| GGUF | Q5_K_M | 50.0 | |
| PART 1 PART 2 | Q6_K | 58.0 | very good quality |
| PART 1 PART 2 | Q8_0 | 75.1 | 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.