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---
base_model: GreenerPastures/Golden-Curry-12B
datasets:
- Mielikki/Erebus-87k
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- NewEden/Kalo-Opus-Instruct-22k-Refusal-Murdered
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- NewEden/Gryphe-Sonnet-3.5-35k-Subset
- Nitral-AI/GU_Instruct-ShareGPT
- Nitral-AI/Medical_Instruct-ShareGPT
- AquaV/Resistance-Sharegpt
- AquaV/US-Army-Survival-Sharegpt
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- ResplendentAI/bluemoon
- hardlyworking/openerotica-freedomrp-sharegpt-system
- MinervaAI/Aesir-Preview
- anthracite-core/c2_logs_32k_v1.1
- Nitral-AI/Creative_Writing-ShareGPT
- PJMixers/lodrick-the-lafted_OpusStories-Story2Prompt-ShareGPT
- NewEden/Opus-accepted-hermes-rejected-shuffled
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/GreenerPastures/Golden-Curry-12B
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Golden-Curry-12B-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/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Golden-Curry-12B-GGUF/resolve/main/Golden-Curry-12B.Q8_0.gguf) | Q8_0 | 13.1 | 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](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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