What are the hardware requirements for Autogressive 32B?

Autogressive 32B requires approximately 131.5 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Autogressive 32B and how large is it?

Autogressive 32B is developed by Multiverse4FM, a model with 32b 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 Autogressive 32B?

Autogressive 32B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Autogressive 32B by Multiverse4FM — VRAM 131.5GB, 32K context

Name: Autogressive 32B
Author: Multiverse4FM

Autogressive 32B is an open-source language model by Multiverse4FM. Features: 32b LLM, VRAM: 131.5GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.18.

Arxiv:2506.09991 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-32b-in... Conversational Dataset:multiverse4fm/autoregr... Dataset:multiverse4fm/multiver... Dataset:simplescaling/s1k-1.1 Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Multiverse4FM/Autogressive-32B

Autogressive 32B Parameters and Internals

LLM Name	Autogressive 32B
Repository 🤗	https://huggingface.co/Multiverse4FM/Autogressive-32B
Base Model(s)	Qwen/Qwen2.5-32B-Instruct Qwen/Qwen2.5-32B-Instruct
Model Size	32b
Required VRAM	131.5 GB
Updated	2026-07-31
Maintainer	Multiverse4FM
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.5 GB: 1-of-29 4.7 GB: 2-of-29 4.5 GB: 3-of-29 4.5 GB: 4-of-29 4.7 GB: 5-of-29 4.5 GB: 6-of-29 4.5 GB: 7-of-29 4.7 GB: 8-of-29 4.5 GB: 9-of-29 4.5 GB: 10-of-29 4.7 GB: 11-of-29 4.5 GB: 12-of-29 4.5 GB: 13-of-29 4.7 GB: 14-of-29 4.5 GB: 15-of-29 4.5 GB: 16-of-29 4.7 GB: 17-of-29 4.5 GB: 18-of-29 4.5 GB: 19-of-29 4.7 GB: 20-of-29 4.5 GB: 21-of-29 4.5 GB: 22-of-29 4.7 GB: 23-of-29 4.5 GB: 24-of-29 4.5 GB: 25-of-29 4.7 GB: 26-of-29 4.5 GB: 27-of-29 4.5 GB: 28-of-29 3.7 GB: 29-of-29 0.0 GB
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|fim_pad\|>
Vocabulary Size	152064
Torch Data Type	float32
Errors	replace

Rank the Autogressive 32B 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

Best Alternatives to Autogressive 32B

Best Alternatives	Context / RAM	Downloads	Likes
...y Qwen2.5coder 32B V24.1q 200K	195K / 65.8 GB	9	2
...wen2.5 32B Inst BaseMerge TIES	128K / 65.8 GB	33	17
...wen2.5 32B Inst BaseMerge TIES	128K / 65.8 GB	51	4
Franqwenstein 35B	128K / 69.8 GB	5	8
ELYZA Thinking 1.0 Qwen 32B	128K / 65.8 GB	23	10
Archaeo 32B KTO	128K / 65.8 GB	17	4
Hamanasu Magnum QwQ 32B	128K / 65.8 GB	295	9
Qwen2.5 32B Gokgok Step3	128K / 65.7 GB	6	0
Openthaigpt R1 32B Instruct	128K / 65.8 GB	33	8
Qwen2.5 32B Dark Days Stage2	128K / 65.8 GB	9	0

Note: green Score (e.g. "73.2") means that the model is better than Multiverse4FM/Autogressive-32B.