base_model: Nekochu/Luminia-8B-RP
datasets:
- Nekochu/Luminia-mixture
- UnfilteredAI/DAN
- mpingale/mental-health-chat-dataset
- Amod/mental_health_counseling_conversations
- heliosbrahma/mental_health_chatbot_dataset
- victunes/nart-100k-synthetic-buddy-mixed-names
- Falah/Mental_health_dataset4Fine_Tuning
- EmoCareAI/Psych8k
- samhog/psychology-10k
- Doctor-Shotgun/no-robots-sharegpt
- Gryphe/Opus-WritingPrompts
- NobodyExistsOnTheInternet/ToxicQAFinal
- meseca/opus-instruct-9k
- PJMixers/grimulkan_theory-of-mind-ShareGPT
- CapybaraPure/Decontaminated-ShareGPT
- MinervaAI/Aesir-Preview
- Epiculous/Gnosis
- Norquinal/claude_multiround_chat_30k
- Locutusque/hercules-v5.0
- G-reen/Duet-v0.5
- cgato/SlimOrcaDedupCleaned
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- ChaoticNeutrals/Synthetic-Dark-RP
- ChaoticNeutrals/Synthetic-RP
- ChaoticNeutrals/Luminous_Opus
- kalomaze/Opus_Instruct_25k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- llama-factory
- lora
- not-for-all-audiences
- nsfw
About
weighted/imatrix quants of https://huggingface.co/Nekochu/Luminia-8B-RP
static quants are available at https://huggingface.co/mradermacher/Luminia-8B-RP-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 | 2.1 | for the desperate |
| GGUF | i1-IQ1_M | 2.3 | mostly desperate |
| GGUF | i1-IQ2_XXS | 2.5 | |
| GGUF | i1-IQ2_XS | 2.7 | |
| GGUF | i1-IQ2_S | 2.9 | |
| GGUF | i1-IQ2_M | 3.0 | |
| GGUF | i1-Q2_K_S | 3.1 | very low quality |
| GGUF | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 3.4 | lower quality |
| GGUF | i1-IQ3_XS | 3.6 | |
| GGUF | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 3.8 | beats Q3_K* |
| GGUF | i1-IQ3_M | 3.9 | |
| GGUF | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 4.5 | |
| GGUF | i1-Q4_0 | 4.8 | fast, low quality |
| GGUF | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| GGUF | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 5.0 | fast, recommended |
| GGUF | i1-Q4_1 | 5.2 | |
| GGUF | i1-Q5_K_S | 5.7 | |
| GGUF | i1-Q5_K_M | 5.8 | |
| GGUF | i1-Q6_K | 6.7 | practically like static Q6_K |
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. 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.