What are the hardware requirements for RAISED Mistral Nemo SFT?

RAISED Mistral Nemo SFT requires approximately 24.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed RAISED Mistral Nemo SFT and how large is it?

RAISED Mistral Nemo SFT is developed by FinaPolat, a model with 12.2b 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 RAISED Mistral Nemo SFT?

RAISED Mistral Nemo SFT is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

RAISED Mistral Nemo SFT by FinaPolat — VRAM 24.5GB, 128K context

Name: RAISED Mistral Nemo SFT
Author: FinaPolat

RAISED Mistral Nemo SFT is an open-source language model by FinaPolat. Features: 12.2b LLM, VRAM: 24.5GB, Context: 128K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.23.

4bit Conversational En Endpoints compatible Instruct Mistral Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/FinaPolat/RAISED_Mistral-Nemo_SFT

RAISED Mistral Nemo SFT Parameters and Internals

LLM Name	RAISED Mistral Nemo SFT
Repository 🤗	https://huggingface.co/FinaPolat/RAISED_Mistral-Nemo_SFT
Base Model(s)	unsloth/mistral-nemo-instruct-2407-bnb-4bit unsloth/mistral-nemo-instruct-2407-bnb-4bit
Model Size	12.2b
Required VRAM	24.5 GB
Updated	2026-08-02
Maintainer	FinaPolat
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
Supported Languages	en
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the RAISED Mistral Nemo SFT capabilities

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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

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