What are the hardware requirements for Sensualize Mixtral AWQ?

Sensualize Mixtral AWQ requires approximately 24.7 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 Sensualize Mixtral AWQ and how large is it?

Sensualize Mixtral AWQ is developed by TheBloke, a model with 46.7b 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 Sensualize Mixtral AWQ?

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

Sensualize Mixtral AWQ by TheBloke — VRAM 24.7GB, 32K context

Name: Sensualize Mixtral AWQ
Author: TheBloke

Sensualize Mixtral AWQ is an open-source language model by TheBloke. Features: 46.7b LLM, VRAM: 24.7GB, Context: 32K, License: cc-by-nc-4.0, Quantized, LLM Explorer Score: 0.1.

4-bit Awq Base model:quantized:sao10k/se... Base model:sao10k/sensualize-m... Dataset:nobodyexistsontheinter... Mixtral Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TheBloke/Sensualize-Mixtral-AWQ

Sensualize Mixtral AWQ Parameters and Internals

Model Type

mixtral

Additional Notes

Experimental model, trained using Alpaca format. Roleplay based model, specifically ERP type, with varied performance depending on prompts. Recommended settings include Universal-Light or Universal-Creative in SillyTavern.

Training Details

Data Sources:

Randomised subset of Full120k - 60K Samples, Own NSFW Instruct & De-Alignment Data

Data Volume:

80M Tokens

Methodology:

Alpaca format using Charles Goddard's ZLoss and Megablocks-based fork of transformers.

Training Time:

12 hours

Hardware Used:

2xA100s at batch size 5, grad 5

Input Output

Input Format:

### Instruction: {system_message} ### Input: {prompt} ### Response:

Output Format:

Response

Performance Tips:

With the right settings this model can shine. I recommend Universal-Light or Universal-Creative in SillyTavern.

LLM Name	Sensualize Mixtral AWQ
Repository 🤗	https://huggingface.co/TheBloke/Sensualize-Mixtral-AWQ
Model Name	Sensualize Mixtral
Model Creator	Saofiq
Base Model(s)	Sensualize Mixtral Bf16 Sao10K/Sensualize-Mixtral-bf16
Model Size	46.7b
Required VRAM	24.7 GB
Updated	2026-06-29
Maintainer	TheBloke
Model Type	mixtral
Model Files	10.0 GB: 1-of-3 10.0 GB: 2-of-3 4.7 GB: 3-of-3
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
License	cc-by-nc-4.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Sensualize Mixtral AWQ 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 Sensualize Mixtral AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...kaLM Mixtral 8x7B V0.2 DPO AWQ	32K / 24.7 GB	6	0
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	5	0
Karakuri Lm 8x7b Chat V0.1 AWQ	32K / 24.7 GB	7	0
Dolphin 2.7 Mixtral 8x7b AWQ	32K / 24.7 GB	2550	23
Mixtral Instruct AWQ	32K / 24.7 GB	2333	43
Functionary Medium V2.4 AWQ	32K / 24.7 GB	5	3
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1045	59
Mixtral 8x7B Instruct V0.1 AWQ	32K / 24.7 GB	1248	16
H2ogpt Mixtral 8x7b 32K AWQ	32K / 24.7 GB	6	0
Synatra Mixtral 8x7B AWQ	32K / 24.7 GB	8	3

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Sensualize-Mixtral-AWQ.