transformers gguf en ko ja zh dataset:4DR1455/finance_questions dataset:Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted dataset:BCCard/BCCard-Finance-Kor-QnA dataset:CarrotAI/ko-code-alpaca-QA dataset:ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5 dataset:DavidLanz/medical_instruction dataset:Dusker/lawyer-llama dataset:Gryphe/Sonnet3.5-Charcard-Roleplay dataset:HAERAE-HUB/qarv-instruct-ko dataset:HachiML/alpaca_jp_math dataset:Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1 dataset:Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese dataset:beomi/KoAlpaca-v1.1a dataset:codefuse-ai/Evol-instruction-66k dataset:frankminors123/belle-math-zh dataset:gbharti/wealth-alpaca_lora dataset:iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B dataset:jihye-moon/LawQA-Ko dataset:jondurbin/gutenberg-dpo-v0.1 dataset:junyeong-nero/kin_med_100K_edited dataset:kyujinpy/KOR-OpenOrca-Platypus-v3 dataset:lavita/medical-qa-datasets dataset:microsoft/orca-math-word-problems-200k dataset:neural-bridge/rag-dataset-12000 dataset:p1atdev/ichikara-instruction dataset:qiaojin/PubMedQA dataset:shibing624/roleplay-zh-sharegpt-gpt4-data dataset:team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted dataset:ymoslem/Law-StackExchange dataset:zzunyang/LawQA_LawSee base_model:werty1248/Mistral-Nemo-NT-Ko-12B-sft base_model:quantized:werty1248/Mistral-Nemo-NT-Ko-12B-sft license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: werty1248/Mistral-Nemo-NT-Ko-12B-sft
datasets:
- 4DR1455/finance_questions
- Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k
- Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-10.5k-formatted
- BCCard/BCCard-Finance-Kor-QnA
- CarrotAI/ko-code-alpaca-QA
- ChuGyouk/AI_healthcare_QA_samples_Sonnet3.5
- DavidLanz/medical_instruction
- Dusker/lawyer-llama
- Gryphe/Sonnet3.5-Charcard-Roleplay
- HAERAE-HUB/qarv-instruct-ko
- HachiML/alpaca_jp_math
- Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-v0.1
- Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese
- beomi/KoAlpaca-v1.1a
- codefuse-ai/Evol-instruction-66k
- frankminors123/belle-math-zh
- gbharti/wealth-alpaca_lora
- iam-ajaymeena/Self-Instruct-Japanese-Elzya-13B
- jihye-moon/LawQA-Ko
- jondurbin/gutenberg-dpo-v0.1
- junyeong-nero/kin_med_100K_edited
- kyujinpy/KOR-OpenOrca-Platypus-v3
- lavita/medical-qa-datasets
- microsoft/orca-math-word-problems-200k
- neural-bridge/rag-dataset-12000
- p1atdev/ichikara-instruction
- qiaojin/PubMedQA
- shibing624/roleplay-zh-sharegpt-gpt4-data
- team-hatakeyama-phase2/AutoMultiTurnByCalm3-22B-Corrected-reformatted
- ymoslem/Law-StackExchange
- zzunyang/LawQA_LawSee
language:
- en
- ko
- ja
- zh
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/werty1248/Mistral-Nemo-NT-Ko-12B-sft

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-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/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q2_K.gguf) | Q2_K | 4.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.IQ3_XS.gguf) | IQ3_XS | 5.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q3_K_S.gguf) | Q3_K_S | 5.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.IQ3_M.gguf) | IQ3_M | 5.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q3_K_L.gguf) | Q3_K_L | 6.7 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.IQ4_XS.gguf) | IQ4_XS | 6.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q5_K_S.gguf) | Q5_K_S | 8.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q5_K_M.gguf) | Q5_K_M | 8.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Nemo-NT-Ko-12B-sft-GGUF/resolve/main/Mistral-Nemo-NT-Ko-12B-sft.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):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](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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