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---
base_model: Nekochu/Luminia-13B-v3
datasets:
- Nekochu/discord-unstable-diffusion-SD-prompts
- glaiveai/glaive-function-calling-v2
- TIGER-Lab/MathInstruct
- Open-Orca/SlimOrca
- GAIR/lima
- sahil2801/CodeAlpaca-20k
- garage-bAInd/Open-Platypus
language:
- en
library_name: transformers
license: apache-2.0
model_creator: Nekochu
model_name: Luminia 13B v3
model_type: llama2
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request. ### Instruction: {Instruction} {summary}
### input: {category} ### Response: {prompt}'
quantized_by: mradermacher
tags:
- llama-factory
- lora
- generated_from_trainer
- llama2
- llama
- instruct
- finetune
- gpt4
- synthetic data
- stable diffusion
- alpaca
- llm
---
## About
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static quants of https://huggingface.co/Nekochu/Luminia-13B-v3
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Luminia-13B-v3-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Luminia-13B-v3-GGUF/resolve/main/Luminia-13B-v3.Q8_0.gguf) | Q8_0 | 13.9 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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