What are the hardware requirements for MN Slush?

MN Slush requires approximately 24.5 GB of VRAM and supports a context window of 1000K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MN Slush and how large is it?

MN Slush is developed by crestf411, a model with 12b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate MN Slush?

MN Slush is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MN Slush by crestf411 — VRAM 24.5GB, 1000K context

Name: MN Slush
Author: crestf411

MN Slush is an open-source language model by crestf411. Features: 12b LLM, VRAM: 24.5GB, Context: 1000K, Instruction-Based, LLM Explorer Score: 0.22.

Arxiv:2306.01708 Arxiv:2402.12354 Arxiv:2403.00946 Arxiv:2410.21228 Autotrain compatible Base model:finetune:mistralai/... Base model:mistralai/mistral-n... Conversational Dataset:anthracite-org/c2 logs... Dataset:anthracite-org/kalo-op... Dataset:anthracite-org/kalo-op... Dataset:anthracite-org/nopm cl... Dataset:crestf411/limarp-ds Dataset:gryphe/sonnet3.5-charc... Endpoints compatible Instruct Mergekit Mistral Not-for-all-audiences Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/crestf411/MN-Slush

MN Slush Parameters and Internals

LLM Name	MN Slush
Repository 🤗	https://huggingface.co/crestf411/MN-Slush
Base Model(s)	Mistral Nemo Instruct 2407 mistralai/Mistral-Nemo-Instruct-2407
Model Size	12b
Required VRAM	24.5 GB
Updated	2025-09-23
Maintainer	crestf411
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5
Model Architecture	MistralForCausalLM
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.46.2
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the MN Slush capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to MN Slush

Best Alternatives	Context / RAM	Downloads	Likes
...r Nemo 12B Instruct R 21 09 24	1000K / 24.5 GB	396277	141
Qliphoth 12B V1.1	1000K / 24.5 GB	22	3
Qliphoth 12B V1	1000K / 24.5 GB	26	2
SauerkrautLM Nemo 12B Instruct	1000K / 24.5 GB	1473	22
Magnum V4 12B	1000K / 24.5 GB	1116	54
ChatWaifu V1.4	1000K / 24.5 GB	335	20
...g M Claude Opus High Reasoning	1000K / 24.5 GB	86	3
ChatWaifu 12B V2.0	1000K / 24.5 GB	175	22
Mistral Nemo Bophades 12B	1000K / 24.5 GB	18	9
Mistral Nemo Wissenschaft 12B	1000K / 24.5 GB	22	8

Note: green Score (e.g. "73.2") means that the model is better than crestf411/MN-Slush.