What are the hardware requirements for OpenHermesV2 PTBR?

OpenHermesV2 PTBR requires approximately 15 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 OpenHermesV2 PTBR and how large is it?

OpenHermesV2 PTBR is developed by artificialguybr, a model with 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 OpenHermesV2 PTBR?

OpenHermesV2 PTBR is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

OpenHermesV2 PTBR by artificialguybr — VRAM 15GB, 32K context

Name: OpenHermesV2 PTBR
Author: artificialguybr

OpenHermesV2 PTBR is an open-source language model by artificialguybr. Features: 7b LLM, VRAM: 15GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.09.

Base model:finetune:mistralai/... Base model:mistralai/mistral-7... Chatml Conversational Distillation En Endpoints compatible Finetuned Gpt4 Instruct Mistral Pt Pytorch Region:us Safetensors Sharded Synthetic data Tensorflow

Model Card on HF 🤗: https://huggingface.co/artificialguybr/OpenHermesV2-PTBR

OpenHermesV2 PTBR Parameters and Internals

Model Type

instruct, finetune, chatml, gpt4, synthetic data, distillation

Use Cases

Primary Use Cases:

Chatting, Role-playing, Assistance

Additional Notes

Follow Teknium updates in ML and AI on Twitter: https://twitter.com/Teknium1. Support Teknium on Github Sponsors: https://github.com/sponsors/teknium1

Supported Languages

en (English), pt (Portuguese)

Training Details

Data Sources:

GPT-4 generated data, open datasets across the AI landscape

Data Volume:

900,000 entries

Methodology:

Fine-tuning on PT-BR, extensive filtering, conversion to ShareGPT format, transformed by axolotl to use ChatML.

Input Output

Input Format:

ChatML

Accepted Modalities:

text

Output Format:

assistant response in ChatML

Performance Tips:

Use system prompts and multi-turn chat dialog for better engagement.

LLM Name	OpenHermesV2 PTBR
Repository 🤗	https://huggingface.co/artificialguybr/OpenHermesV2-PTBR
Base Model(s)	mistralai/Mistral-7B-v0.1 mistralai/Mistral-7B-v0.1
Model Size	7b
Required VRAM	15 GB
Updated	2026-08-07
Maintainer	artificialguybr
Model Type	mistral
Model Files	9.9 GB: 1-of-2 5.1 GB: 2-of-2 9.9 GB: 1-of-2 5.1 GB: 2-of-2
Supported Languages	en pt
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.34.1
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	float32

Rank the OpenHermesV2 PTBR 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 OpenHermesV2 PTBR

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
MegaBeam Mistral 7B 300K	282K / 14.4 GB	3779	16
MegaBeam Mistral 7B 300K	282K / 14.4 GB	8018	17
Hebrew Mistral 7B 200K	256K / 30 GB	23	15
Astral 256K 7B	250K / 14.4 GB	9	0
Astral 256K 7B V2	250K / 14.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than artificialguybr/OpenHermesV2-PTBR.