What are the hardware requirements for HelpingAI2 9B?

HelpingAI2 9B requires approximately 17.8 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed HelpingAI2 9B and how large is it?

HelpingAI2 9B is developed by OEvortex, a model with 9b 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 HelpingAI2 9B?

HelpingAI2 9B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HelpingAI2 9B by OEvortex — VRAM 17.8GB, 128K context

Name: HelpingAI2 9B
Author: OEvortex

HelpingAI2 9B is an open-source language model by OEvortex. Features: 9b LLM, VRAM: 17.8GB, Context: 128K, License: other, Quantized, LLM Explorer Score: 0.19.

Conversational Dataset:abhaykoul/emotions Dataset:abhaykoul/human-emotio... Dataset:jeankaddour/minipile Dataset:oevortex/emotionalinte... Dataset:oevortex/sentimentsynt... Dataset:oevortex/vortex-mini Emotionally intelligent En Endpoints compatible Eq Gguf Helpingai Llama Model-index Q5 Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/OEvortex/HelpingAI2-9B

HelpingAI2 9B Parameters and Internals

Model Type

Emotionally Intelligent Conversational AI

Use Cases

Areas:

Emotionally intelligent conversations

Primary Use Cases:

Engage in dialogue while displaying emotional intelligence, Recognize and validate user emotions, Provide empathetic responses, Avoid insensitive or harmful speech

Supported Languages

English (high)

Training Details

Data Sources:

OEvortex/SentimentSynth, JeanKaddour/minipile, OEvortex/vortex-mini, OEvortex/EmotionalIntelligence-75K, Abhaykoul/EMOTIONS, Abhaykoul/human-emotion

Methodology:

The model is trained using supervised learning with large dialogue datasets containing emotional labels, reinforcement learning to favour emotionally supportive interactions, and constitution training for stable ethical objectives. Incorporation of psychological resources on emotional intelligence is used to enhance understanding and response capabilities.

Input Output

Input Format:

The model utilizes a chat-based format where the system prompt defines its role and style.

Accepted Modalities:

text

Output Format:

Text

LLM Name	HelpingAI2 9B
Repository 🤗	https://huggingface.co/OEvortex/HelpingAI2-9B
Model Size	9b
Required VRAM	17.8 GB
Updated	2024-12-05
Maintainer	OEvortex
Model Type	llama
Model Files	6.2 GB 5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 2.9 GB: 4-of-4
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	q5\|gguf
Model Architecture	LlamaForCausalLM
License	other
Context Length	131072
Model Max Length	131072
Transformers Version	4.44.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the HelpingAI2 9B capabilities

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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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HelpingAI2 9B	128K / 17.8 GB	64	25
Llama 3 Experiment V1 9B GGUF	8K / 5.4 GB	256	0
HelpingAI 9B	4K / 17.6 GB	80	25
Yi 1.5 9B Chat GGUF	4K / 3.4 GB	1245	8
Yi 1.5 9B Chat GGUF	4K / 3.4 GB	360	2
HelpingAI 9B	4K / 17.6 GB	139	26
Yi 1.5 9B Chat IMat GGUF	4K / 2 GB	164	0
Yi 9B 200K 3.0bpw H6 EXL2	256K / 3.9 GB	6	1
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Faro Yi 9B DPO 6bpw EXL2	32K / 7 GB	3	1