🚀 Exciting Announcement: Introducing HelpingAI-3B-reloaded!
•
2
Abhay Koul
Abhaykoul
AI & ML interests
None yet
Articles
Organizations
Abhaykoul's activity
posted
an
update
3 months ago
Post
2660
Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link : OEvortex/HelpingAI2-9B
Demo Link: Abhaykoul/HelpingAI2
This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.
Key Features:
-----------------
* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
* It gives responses in empathetic and supportive manner.
Must try our demo: Abhaykoul/HelpingAI2
Model Link : OEvortex/HelpingAI2-9B
Demo Link: Abhaykoul/HelpingAI2
This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.
Key Features:
-----------------
* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.
* It gives responses in empathetic and supportive manner.
Must try our demo: Abhaykoul/HelpingAI2
reacted to
their
post with 👀🚀
3 months ago
Post
2812
Introduction to HelpingAI-3B-v2.1 and HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2
replied to
their
post
5 months ago
This is a opensource project
Then why you confusing peoples with custom license. Choose any better license. like Apache 2.0 or any poplar.
My partner has dealt with the licenses. Actually, I'm not sure what he intends to do next, but for now, it is an open-source project.
replied to
their
post
5 months ago
This is a opensource project
reacted to
their
post with 🔥
5 months ago
Post
2495
# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI
If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!
In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench.
## What is HelpingAI 9B?
HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.
## Method
We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:
1. DPO - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.
2. Alpaca - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.
3. SFT - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.
## Evaluation
## Conclusion:
HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.
Thanks!
Model Link: - OEvortex/HelpingAI-9B
Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!
In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench.
## What is HelpingAI 9B?
HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.
## Method
We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:
1. DPO - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.
2. Alpaca - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.
3. SFT - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.
## Evaluation
## Conclusion:
HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.
Thanks!
Model Link: - OEvortex/HelpingAI-9B
Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
-
- 9 replies
posted
an
update
5 months ago
Post
2495
# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI
If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!
In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench.
## What is HelpingAI 9B?
HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.
## Method
We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:
1. DPO - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.
2. Alpaca - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.
3. SFT - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.
## Evaluation
## Conclusion:
HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.
Thanks!
Model Link: - OEvortex/HelpingAI-9B
Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!
In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench.
## What is HelpingAI 9B?
HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.
## Method
We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:
1. DPO - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.
2. Alpaca - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.
3. SFT - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.
## Evaluation
## Conclusion:
HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.
Thanks!
Model Link: - OEvortex/HelpingAI-9B
Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
-
-
-
- 9 replies
reacted to
smangrul's
post with 🤯
7 months ago
Post
3151
Unlocking the Power of locally running Llama-3 8B Model Agents with Chat-UI! 🔥🚀✨
I'm thrilled to share my hackathon-style side project:
1. Finetuning Llama-8B for function calling using PEFT QLoRA as the instruct Llama-3 model doesn't support this. The colab notebook for it is here: https://lnkd.in/ggJMzqh2. 🛠️
2. Finetuned model along with the 4-bit quants here: https://lnkd.in/gNpFKY6V ✨
3. Clone Hugging Face https://lnkd.in/gKBKuUBQ and make it compatible for function calling by building upon the PR https://lnkd.in/gnqFuAd4 for my model and local inferencing usecase using Ollama. This was a steep learning curve wherein I stayed awake the whole night to get it working. 💪🏽
4. Above, I used SerpAPI for web browsing and Mongo DB Atlas free tier for persistence of conversations and assistant configs. 🔎
5. More work is required to switch between using tools and responding directly wherein I see the model breaks. 🧐
How cool is this wherein we are approaching experience akin to ChatGPT while using local hosted agent model running on your laptop! 💻
I'm thrilled to share my hackathon-style side project:
1. Finetuning Llama-8B for function calling using PEFT QLoRA as the instruct Llama-3 model doesn't support this. The colab notebook for it is here: https://lnkd.in/ggJMzqh2. 🛠️
2. Finetuned model along with the 4-bit quants here: https://lnkd.in/gNpFKY6V ✨
3. Clone Hugging Face https://lnkd.in/gKBKuUBQ and make it compatible for function calling by building upon the PR https://lnkd.in/gnqFuAd4 for my model and local inferencing usecase using Ollama. This was a steep learning curve wherein I stayed awake the whole night to get it working. 💪🏽
4. Above, I used SerpAPI for web browsing and Mongo DB Atlas free tier for persistence of conversations and assistant configs. 🔎
5. More work is required to switch between using tools and responding directly wherein I see the model breaks. 🧐
How cool is this wherein we are approaching experience akin to ChatGPT while using local hosted agent model running on your laptop! 💻
- 1 reply
reacted to
their
post with 🔥
7 months ago
Post
2812
Introduction to HelpingAI-3B-v2.1 and HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2
posted
an
update
7 months ago
Post
2812
Introduction to HelpingAI-3B-v2.1 and HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2
These models are designed to engage in dialogues that are not only contextually relevant but also deeply empathetic, tailoring their responses to the emotional state and context of the user. With advanced emotional understanding capabilities, they can comfort, celebrate, and address complex feelings with a level of emotional nuance that is unparalleled in the AI domain.
Emotional Intelligence Capabilities
The core of these models lies in their emotional intelligence capabilities. They exhibit several key traits that enable them to respond emotionally resonant:
- Emotion recognition and validation: The models can identify and validate the emotions expressed by the user, ensuring that their responses are grounded in understanding.
- Empathetic perspective-taking: They are capable of taking the user's perspective, understanding their feelings, and responding with empathy.
- Generating emotionally supportive language: The models can generate language that is supportive and comforting, tailored to the user's emotional state.
- Contextual emotional attunement: They can adapt their communication style to match the emotional context of the conversation, ensuring relevance and appropriateness.
- Using appropriate tone, word choice, and emotional expression**: The models are adept at using the right tone, words, and emotional expressions to convey their messages effectively.
Examples of Emotionally Intelligent Responses
Let's take a look at how these models can adapt their communication style with emotional nuance:
- Comforting someone grieving: The models can offer empathetic responses, acknowledging the user's feelings and suggesting practical ways to cope with grief.
- Celebrating positive news: They can generate responses that are joyful and supportive, encouraging the user to enjoy their positive experiences.
OEvortex/HelpingAI-3B-v2.1
OEvortex/HelpingAI-3B-v2.2