Project Fluently
non-profit
AI & ML interests
Finetune Diffusion, Train Diffusion
fluently's activity
ameerazam08Β
posted
an
update
7 days ago
Post
3701
π₯ THE WAIT IS OVER... HAI-SER IS HERE! π₯
Yo fam, this ain't just another AI dropβ this is the FUTURE of emotional intelligence! π
Introducing HAI-SER, powered by Structured Emotional Reasoning (SER), the next-level AI that doesnβt just understand your wordsβit feels you, analyzes your emotions, and helps you navigate lifeβs toughest moments. π‘
π₯ What makes HAI-SER a game-changer?
πΉ Emotional Vibe Check β Gets the mood, energy, and whatβs really going on π
πΉ Mind-State Analysis β Breaks down your thoughts, beliefs, and patterns π€―
πΉ Root Cause Deep-Dive β Unpacks the WHY behind your emotions π‘
πΉ Impact Check β Sees how itβs affecting your life and mental health π
πΉ Safety Check β Prioritizes your well-being and crisis management π¨
πΉ Healing Game Plan β Custom strategies to help you bounce back πͺ
πΉ Growth Potential β Turns struggles into opportunities for self-improvement π
πΉ How to Approach β Teaches you and others how to communicate and heal π€
πΉ Personalized Response β Not just generic adviceβreal talk, tailored to YOU π―
No more robotic AI responses. No more surface-level advice. HAI-SER gets deep, analyzing emotions with precision and giving real, actionable support.
This ainβt just AIβthis is your digital therapist, life coach, and hype squad all in one. Whether itβs mental health, career struggles, relationships, or personal growth, HAI-SER has your back.
π The future of emotionally intelligent AI is HERE.
Are you ready? π₯π―
HelpingAI/HAI-SER
Yo fam, this ain't just another AI dropβ this is the FUTURE of emotional intelligence! π
Introducing HAI-SER, powered by Structured Emotional Reasoning (SER), the next-level AI that doesnβt just understand your wordsβit feels you, analyzes your emotions, and helps you navigate lifeβs toughest moments. π‘
π₯ What makes HAI-SER a game-changer?
πΉ Emotional Vibe Check β Gets the mood, energy, and whatβs really going on π
πΉ Mind-State Analysis β Breaks down your thoughts, beliefs, and patterns π€―
πΉ Root Cause Deep-Dive β Unpacks the WHY behind your emotions π‘
πΉ Impact Check β Sees how itβs affecting your life and mental health π
πΉ Safety Check β Prioritizes your well-being and crisis management π¨
πΉ Healing Game Plan β Custom strategies to help you bounce back πͺ
πΉ Growth Potential β Turns struggles into opportunities for self-improvement π
πΉ How to Approach β Teaches you and others how to communicate and heal π€
πΉ Personalized Response β Not just generic adviceβreal talk, tailored to YOU π―
No more robotic AI responses. No more surface-level advice. HAI-SER gets deep, analyzing emotions with precision and giving real, actionable support.
This ainβt just AIβthis is your digital therapist, life coach, and hype squad all in one. Whether itβs mental health, career struggles, relationships, or personal growth, HAI-SER has your back.
π The future of emotionally intelligent AI is HERE.
Are you ready? π₯π―
HelpingAI/HAI-SER
- 5 replies
Post
773
Hey everyone π€!
Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone
This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone
This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
ehristoforuΒ
posted
an
update
about 2 months ago
Post
3202
βοΈ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset
β Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
π€― Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
π€ For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
βοΈ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
fluently-sets/ultraset
β Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
π€― Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
π€ For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
βοΈ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
Post
1926
π₯ BIG ANNOUNCEMENT: THE HELPINGAI API IS LIVE! π₯
Yo, the moment youβve all been waiting for is here! π The HelpingAI API is now LIVE and ready to level up your projects! π₯ Weβre bringing that next-level AI goodness straight to your fingertips. π―
No more waitingβ itβs time to build something epic! π
From now on, you can integrate our cutting-edge AI models into your own applications, workflows, and everything in between. Whether youβre a developer, a creator, or just someone looking to make some serious moves, this is your chance to unlock the full potential of emotional intelligence and adaptive AI.
Check out the docs π₯ and letβs get to work! π
π Check out the docs and start building (https://helpingai.co/docs)
π Visit the HelpingAI website (https://helpingai.co/)
Yo, the moment youβve all been waiting for is here! π The HelpingAI API is now LIVE and ready to level up your projects! π₯ Weβre bringing that next-level AI goodness straight to your fingertips. π―
No more waitingβ itβs time to build something epic! π
From now on, you can integrate our cutting-edge AI models into your own applications, workflows, and everything in between. Whether youβre a developer, a creator, or just someone looking to make some serious moves, this is your chance to unlock the full potential of emotional intelligence and adaptive AI.
Check out the docs π₯ and letβs get to work! π
π Check out the docs and start building (https://helpingai.co/docs)
π Visit the HelpingAI website (https://helpingai.co/)
-
- 6 replies
eienmojikiΒ
posted
an
update
about 2 months ago
Post
1518
π Introducing 2048 Game API: A RESTful API for the Classic Puzzle Game π§©
I'm excited to share my latest project, 2048 Game API, a RESTful API that allows you to create, manage, and play games of 2048, a popular puzzle game where players slide numbered tiles to combine them and reach the goal of getting a tile with the value of 2048.
β Features
Create new games with customizable board sizes (3-8)
Make moves (up, down, left, right) and get the updated game state
Get the current game state, including the board, score, and game over status
Delete games
Generate images of the game board with customizable themes (light and dark)
π API Endpoints
𧩠Example Use Cases
- Create a new game with a 4x4 board:
- Make a move up:
- Get the current game state:
π Try it out!
- Demo: eienmojiki/2048
- Source: https://github.com/kogakisaki/koga-2048
- You can try out the API by running the server locally or using a tool like Postman to send requests to the API. I hope you enjoy playing 2048 with this API!
Let me know if you have any questions or feedback!
π§ Mouse1 is our friendπ§
I'm excited to share my latest project, 2048 Game API, a RESTful API that allows you to create, manage, and play games of 2048, a popular puzzle game where players slide numbered tiles to combine them and reach the goal of getting a tile with the value of 2048.
β Features
Create new games with customizable board sizes (3-8)
Make moves (up, down, left, right) and get the updated game state
Get the current game state, including the board, score, and game over status
Delete games
Generate images of the game board with customizable themes (light and dark)
π API Endpoints
POST /api/games - Create a new gameGET /api/games/:gameId - Get the current game statePOST /api/games/:gameId/move - Make a move (up, down, left, right)DELETE /api/games/:gameId - Delete a gameGET /api/games/:gameId/image - Generate an image of the game board𧩠Example Use Cases
- Create a new game with a 4x4 board:
curl -X POST -H "Content-Type: application/json" -d '{"size": 4}' http://localhost:3000/api/games- Make a move up:
curl -X POST -H "Content-Type: application/json" -d '{"direction": "up"}' http://localhost:3000/api/games/:gameId/move- Get the current game state:
curl -X GET http://localhost:3000/api/games/:gameIdπ Try it out!
- Demo: eienmojiki/2048
- Source: https://github.com/kogakisaki/koga-2048
- You can try out the API by running the server locally or using a tool like Postman to send requests to the API. I hope you enjoy playing 2048 with this API!
Let me know if you have any questions or feedback!
π§ Mouse1 is our friendπ§
Post
7831
Realtime Whisper Large v3 Turbo Demo:
It transcribes audio in about 0.3 seconds.
KingNish/Realtime-whisper-large-v3-turbo
It transcribes audio in about 0.3 seconds.
KingNish/Realtime-whisper-large-v3-turbo
Post
8243
Exciting news! Introducing super-fast AI video assistant, currently in beta. With a minimum latency of under 500ms and an average latency of just 600ms.
DEMO LINK:
KingNish/Live-Video-Chat
DEMO LINK:
KingNish/Live-Video-Chat
- 1 reply
Post
3262
A super good and fast image inpainting demo is here.
Its' super cool and realistic.
Demo by @OzzyGT (Must try):
OzzyGT/diffusers-fast-inpaint
Its' super cool and realistic.
Demo by @OzzyGT (Must try):
OzzyGT/diffusers-fast-inpaint
Post
3585
Mistral Nemo is better than many models in 1st grader level reasoning.
Post
3910
I am experimenting with Flux and trying to push it to its limits without training (as I am GPU-poor π
).
I found some flaws in the pipelines, which I resolved, and now I am able to generate an approx similar quality image as Flux Schnell 4 steps in just 1 step.
Demo Link:
KingNish/Realtime-FLUX
I found some flaws in the pipelines, which I resolved, and now I am able to generate an approx similar quality image as Flux Schnell 4 steps in just 1 step.
Demo Link:
KingNish/Realtime-FLUX
- 1 reply
Post
1892
I am excited to announce a major speed updated in Voicee, a superfast voice assistant.
It has now achieved latency <250 ms.
While its average latency is about 500ms.
KingNish/Voicee
This become Possible due to newly launched @sambanovasystems cloud.
You can also use your own API Key to get fastest speed.
You can get on from here: https://cloud.sambanova.ai/apis
For optimal performance use Google Chrome.
Please try Voicee and share your valuable feedback to help me further improve its performance and usability.
Thank you!
It has now achieved latency <250 ms.
While its average latency is about 500ms.
KingNish/Voicee
This become Possible due to newly launched @sambanovasystems cloud.
You can also use your own API Key to get fastest speed.
You can get on from here: https://cloud.sambanova.ai/apis
For optimal performance use Google Chrome.
Please try Voicee and share your valuable feedback to help me further improve its performance and usability.
Thank you!
Post
1350
Hey everyone π€!
We (finegrain) have created some custom ComfyUI nodes to use our refiners micro-framework inside comfy! π
We only support our new Box Segmenter at the moment, but we're thinking of adding more nodes since there seems to be a demand for it. We leverage the new (beta) Comfy Registry to host our nodes. They are available at: https://registry.comfy.org/publishers/finegrain/nodes/comfyui-refiners. You can install them by running:
Or by unzipping the archive you can download by clicking "Download Latest" into your
We are eager to hear your feedbacks and suggestions for new nodes and how you'll use them! π
We (finegrain) have created some custom ComfyUI nodes to use our refiners micro-framework inside comfy! π
We only support our new Box Segmenter at the moment, but we're thinking of adding more nodes since there seems to be a demand for it. We leverage the new (beta) Comfy Registry to host our nodes. They are available at: https://registry.comfy.org/publishers/finegrain/nodes/comfyui-refiners. You can install them by running:
comfy node registry-install comfyui-refinersOr by unzipping the archive you can download by clicking "Download Latest" into your
custom_nodes comfy folder.We are eager to hear your feedbacks and suggestions for new nodes and how you'll use them! π
Post
4425
Hey everyone π€!
Check out this awesome new model for object segmentation!
finegrain/finegrain-object-cutter.
We (finegrain) have trained this new model in partnership with Nfinite and some of their synthetic data, the resulting model is incredibly accurate π.
Itβs all open source under the MIT license ( finegrain/finegrain-box-segmenter), complete with a test set tailored for e-commerce ( finegrain/finegrain-product-masks-lite). Have fun experimenting with it!
Check out this awesome new model for object segmentation!
finegrain/finegrain-object-cutter.
We (finegrain) have trained this new model in partnership with Nfinite and some of their synthetic data, the resulting model is incredibly accurate π.
Itβs all open source under the MIT license ( finegrain/finegrain-box-segmenter), complete with a test set tailored for e-commerce ( finegrain/finegrain-product-masks-lite). Have fun experimenting with it!
Post
2710
Plugins in NiansuhAI
Plugin Names:
1. WebSearch: Searches the web using search engines.
2. Calculator: Evaluates mathematical expressions, extending the base Tool class.
3. WebBrowser: Extracts and summarizes information from web pages.
4. Wikipedia: Retrieves information from Wikipedia using its API.
5. Arxiv: Searches and fetches article information from Arxiv.
6. WolframAlphaTool: Provides answers on math, science, technology, culture, society, and everyday life.
These plugins currently support the GPT-4O-2024-08-06 model, which also supports image analysis.
Try it now: https://huggingface.co/spaces/NiansuhAI/chat
Similar to: https://hf.co/chat
Plugin Names:
1. WebSearch: Searches the web using search engines.
2. Calculator: Evaluates mathematical expressions, extending the base Tool class.
3. WebBrowser: Extracts and summarizes information from web pages.
4. Wikipedia: Retrieves information from Wikipedia using its API.
5. Arxiv: Searches and fetches article information from Arxiv.
6. WolframAlphaTool: Provides answers on math, science, technology, culture, society, and everyday life.
These plugins currently support the GPT-4O-2024-08-06 model, which also supports image analysis.
Try it now: https://huggingface.co/spaces/NiansuhAI/chat
Similar to: https://hf.co/chat
Post
3593
Introducing Voicee, A superfast voice fast assistant.
KingNish/Voicee
It achieved latency <500 ms.
While its average latency is 700ms.
It works best in Google Chrome.
Please try and give your feedbacks.
Thank you. π€
KingNish/Voicee
It achieved latency <500 ms.
While its average latency is 700ms.
It works best in Google Chrome.
Please try and give your feedbacks.
Thank you. π€
- 3 replies
ehristoforuΒ
updated
a
Space
6 months ago
Post
3124
Introducing HelpingAI2-9B, an emotionally intelligent LLM.
Model Link : https://huggingface.co/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 : https://huggingface.co/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
- 1 reply
Post
2575
Hey everyone π€!
Check out this cool new space from Finegrain: finegrain/finegrain-object-eraser
Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: https://github.com/finegrain-ai/refiners)
Check out this cool new space from Finegrain: finegrain/finegrain-object-eraser
Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: https://github.com/finegrain-ai/refiners)
- 2 replies
ehristoforuΒ
posted
an
update
7 months ago
Post
4345
π Hello from Project Fluently Team!
β¨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.
π οΈ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.
π Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.
π§ Today, without demo images (there wasnβt much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.
π» Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
β¨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.
π οΈ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.
π Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.
π§ Today, without demo images (there wasnβt much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.
π» Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
- 1 reply