What are the hardware requirements for Nous Hermes 2 Mixtral 8x7B DPO AWQ?

Nous Hermes 2 Mixtral 8x7B DPO 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 Nous Hermes 2 Mixtral 8x7B DPO AWQ and how large is it?

Nous Hermes 2 Mixtral 8x7B DPO 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 Nous Hermes 2 Mixtral 8x7B DPO AWQ?

Nous Hermes 2 Mixtral 8x7B DPO AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Nous Hermes 2 Mixtral 8x7B DPO AWQ by TheBloke — VRAM 24.7GB, 32K context

Name: Nous Hermes 2 Mixtral 8x7B DPO AWQ
Author: TheBloke

Nous Hermes 2 Mixtral 8x7B DPO AWQ is an open-source language model by TheBloke. Features: 46.7b LLM, VRAM: 24.7GB, Context: 32K, License: apache-2.0, MoE, Quantized, LLM Explorer Score: 0.1.

4-bit Awq Base model:nousresearch/nous-h... Base model:quantized:nousresea... Chatml Conversational Distillation Dpo En Finetuned Gpt4 Instruct Mixtral Moe Quantized Region:us Rlhf Safetensors Sharded Synthetic data Tensorflow

Model Card on HF 🤗: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ

Nous Hermes 2 Mixtral 8x7B DPO AWQ Parameters and Internals

Model Type

mixtral, instruct, finetune, chatml, DPO, RLHF, gpt4, synthetic data, distillation

Additional Notes

Not compatible with macOS for AWQ, use GGUF for macOS.

Supported Languages

en (fluent)

Training Details

Data Sources:

GPT-4 generated data, other high quality data from open datasets

Data Volume:

1 million entries

Methodology:

SFT+DPO

Context Length:

8192

Model Architecture:

Mixtral 8x7B MoE LLM

Input Output

Input Format:

ChatML

Accepted Modalities:

text

Output Format:

formatted text

Performance Tips:

Use AutoAWQ v0.1.8 or later for best performance.

LLM Name	Nous Hermes 2 Mixtral 8x7B DPO AWQ
Repository 🤗	https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ
Model Name	Nous Hermes 2 Mixtral 8X7B DPO
Model Creator	NousResearch
Base Model(s)	Nous Hermes 2 Mixtral 8x7B DPO NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
Model Size	46.7b
Required VRAM	24.7 GB
Updated	2026-08-06
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
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	float16

Rank the Nous Hermes 2 Mixtral 8x7B DPO 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 Nous Hermes 2 Mixtral 8x7B DPO 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/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ.