What are the hardware requirements for ChaoticSoliloquy 4x8B 6bpw H6 EXL2?

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 requires approximately 19.4 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ChaoticSoliloquy 4x8B 6bpw H6 EXL2 and how large is it?

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 is developed by JayhC, a model with 8b 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 ChaoticSoliloquy 4x8B 6bpw H6 EXL2?

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 by JayhC — VRAM 19.4GB, 8K context

Name: ChaoticSoliloquy 4x8B 6bpw H6 EXL2
Author: JayhC

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 is an open-source language model by JayhC. Features: 8b LLM, VRAM: 19.4GB, Context: 8K, License: llama3, MoE, Quantized, LLM Explorer Score: 0.12.

6-bit Conversational En Endpoints compatible Exl2 Mixtral Moe Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/JayhC/ChaoticSoliloquy-4x8B-6bpw-h6-exl2

ChaoticSoliloquy 4x8B 6bpw H6 EXL2 Parameters and Internals

Model Type

Mixture of Experts, RP-oriented

Use Cases

Primary Use Cases:

RP/ERP tasks

Supported Languages

en (high)

Training Details

Methodology:

exl2 quantization of ChaoticSoliloquy-4x8B using default exllamav2 calibration dataset

Model Architecture:

MoE with gate_mode random, dtype bfloat16, experts_per_token 2

Input Output

Input Format:

Llama 3

LLM Name	ChaoticSoliloquy 4x8B 6bpw H6 EXL2
Repository 🤗	https://huggingface.co/JayhC/ChaoticSoliloquy-4x8B-6bpw-h6-exl2
Model Size	8b
Required VRAM	19.4 GB
Updated	2026-07-29
Maintainer	JayhC
Model Type	mixtral
Model Files	8.6 GB: 1-of-3 8.6 GB: 2-of-3 2.2 GB: 3-of-3
Supported Languages	en
Quantization Type	exl2
Model Architecture	MixtralForCausalLM
License	llama3
Context Length	8192
Model Max Length	8192
Transformers Version	4.37.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|begin_of_text\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the ChaoticSoliloquy 4x8B 6bpw H6 EXL2 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

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...oquy 4x8B Bpw600 H6 EXL2 Rpcal	8K / 19.4 GB	5	2
...oquy 4x8B Bpw500 H6 EXL2 Rpcal	8K / 16.4 GB	4	1
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Note: green Score (e.g. "73.2") means that the model is better than JayhC/ChaoticSoliloquy-4x8B-6bpw-h6-exl2.