What are the hardware requirements for Mixtral Extraction 4x7B Instruct V0.1?

Mixtral Extraction 4x7B Instruct V0.1 requires approximately 48.5 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 Mixtral Extraction 4x7B Instruct V0.1 and how large is it?

Mixtral Extraction 4x7B Instruct V0.1 is developed by mmnga, a model with 24.2b 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 Mixtral Extraction 4x7B Instruct V0.1?

Mixtral Extraction 4x7B Instruct V0.1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral Extraction 4x7B Instruct V0.1 by mmnga — VRAM 48.5GB, 32K context

Name: Mixtral Extraction 4x7B Instruct V0.1
Author: mmnga

Mixtral Extraction 4x7B Instruct V0.1 is an open-source language model by mmnga. Features: 24.2b LLM, VRAM: 48.5GB, Context: 32K, License: apache-2.0, MoE, Instruction-Based, LLM Explorer Score: 0.1.

Conversational De En Es Fr Instruct It Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mmnga/Mixtral-Extraction-4x7B-Instruct-v0.1

Mixtral Extraction 4x7B Instruct V0.1 Parameters and Internals

Model Type

instruction-following

Additional Notes

This model is experimental and allows extraction of experts from the base model using bit strings.

Supported Languages

fr (Proficient), it (Proficient), de (Proficient), es (Proficient), en (Proficient)

Input Output

Accepted Modalities:

text

LLM Name	Mixtral Extraction 4x7B Instruct V0.1
Repository 🤗	https://huggingface.co/mmnga/Mixtral-Extraction-4x7B-Instruct-v0.1
Model Size	24.2b
Required VRAM	48.5 GB
Updated	2026-06-25
Maintainer	mmnga
Model Type	mixtral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-10 5.0 GB: 2-of-10 5.0 GB: 3-of-10 4.9 GB: 4-of-10 5.0 GB: 5-of-10 5.0 GB: 6-of-10 5.0 GB: 7-of-10 5.0 GB: 8-of-10 5.0 GB: 9-of-10 3.7 GB: 10-of-10
Supported Languages	fr it de es en
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Mixtral Extraction 4x7B Instruct V0.1 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 Mixtral Extraction 4x7B Instruct V0.1

Best Alternatives	Context / RAM	Downloads	Likes
Proto Athena 4x7B	32K / 48.4 GB	5	0
Proto Athena V0.2 4x7B	32K / 48.4 GB	8	0
...erges MoE 4x7b V10 Mixtralv0.3	32K / 48.3 GB	6	0
Sixtyoneeighty 4x7B V1	32K / 48.3 GB	62	0
MixtureofMerges MoE 4x7bRP V11	32K / 48.3 GB	4	1
West Ramen 7Bx4	32K / 48.7 GB	53	0
Eclipse Mistral 4x7b	32K / 48.5 GB	16	1
MoE Merging	32K / 48.3 GB	70	0
Boundary Mistral 4x7b MoE	32K / 48.7 GB	117	1
...tral Fusion 4x7B Instruct V0.1	32K / 48.5 GB	8	18

Note: green Score (e.g. "73.2") means that the model is better than mmnga/Mixtral-Extraction-4x7B-Instruct-v0.1.