What are the hardware requirements for Ms32 Final TEXTONLY?

Ms32 Final TEXTONLY requires approximately 47.3 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 Ms32 Final TEXTONLY and how large is it?

Ms32 Final TEXTONLY is developed by allura-forge, a model with 24b 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 Ms32 Final TEXTONLY?

Ms32 Final TEXTONLY is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Ms32 Final TEXTONLY by allura-forge — VRAM 47.3GB, 128K context

Name: Ms32 Final TEXTONLY
Author: allura-forge

Ms32 Final TEXTONLY is an open-source language model by allura-forge. Features: 24b LLM, VRAM: 47.3GB, Context: 128K, Instruction-Based, LLM Explorer Score: 0.19.

Base model:finetune:mistralai/... Base model:mistralai/mistral-s... Instruct Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/allura-forge/ms32-final-TEXTONLY

Ms32 Final TEXTONLY Parameters and Internals

LLM Name	Ms32 Final TEXTONLY
Repository 🤗	https://huggingface.co/allura-forge/ms32-final-TEXTONLY
Base Model(s)	mistralai/Mistral-Small-3.2-24B-Instruct-2506 mistralai/Mistral-Small-3.2-24B-Instruct-2506
Model Size	24b
Required VRAM	47.3 GB
Updated	2026-07-30
Maintainer	allura-forge
Model Type	mistral
Instruction-Based	Yes
Model Files	4.0 GB: 1-of-10 4.8 GB: 2-of-10 4.9 GB: 3-of-10 4.8 GB: 4-of-10 4.8 GB: 5-of-10 4.9 GB: 6-of-10 4.8 GB: 7-of-10 4.8 GB: 8-of-10 4.9 GB: 9-of-10 4.6 GB: 10-of-10
Model Architecture	MistralForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.50.0.dev0
Tokenizer Class	LlamaTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Ms32 Final TEXTONLY 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 Ms32 Final TEXTONLY

Best Alternatives	Context / RAM	Downloads	Likes
...ral Nemo Instruct 24B Merge V1	1000K / 24.6 GB	9	0
...all 3.1 24B Instruct 2503 Text	128K / 47.3 GB	17	0
Cydonia 24B V4.3	128K / 47.3 GB	3652	138
Slimaki 24B V1.2	128K / 47.3 GB	28	6
Neural V1 24B	128K / 47.3 GB	9	3
MS3.2 PaintedFantasy V4.1 24B	128K / 47.3 GB	80	18
Cydonia 24B V4.1	128K / 47.3 GB	2162	80
Slimaki 24B V1	128K / 47.3 GB	15	10
Asmodeus 24B V1	128K / 47.3 GB	34	12
MS3.2 PaintedFantasy V3 24B	128K / 47.3 GB	28	35

Note: green Score (e.g. "73.2") means that the model is better than allura-forge/ms32-final-TEXTONLY.