AI & ML interests

Finetune Diffusion, Train Diffusion

fluently's activity

Nymboย 
posted an update 4 days ago
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PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.
ehristoforuย 
posted an update 2 months ago
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Introducing our first standalone model โ€“ FluentlyLM Prinum

Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.

General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT

Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.

Evolution:
๐Ÿ† 12th place in the Open LLM Leaderboard ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)

Detailed results and comparisons are presented in Pic. 3.

Links:
- Model: fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo
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ameerazam08ย 
posted an update 3 months ago
Abhaykoulย 
posted an update 3 months ago
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๐Ÿ”ฅ THE WAIT IS OVER... HAI-SER IS HERE! ๐Ÿ”ฅ

Yo fam, this ain't just another AI dropโ€” this is the FUTURE of emotional intelligence! ๐Ÿš€

Introducing HAI-SER, powered by Structured Emotional Reasoning (SER), the next-level AI that doesnโ€™t just understand your wordsโ€”it feels you, analyzes your emotions, and helps you navigate lifeโ€™s toughest moments. ๐Ÿ’ก

๐Ÿ’ฅ What makes HAI-SER a game-changer?
๐Ÿ”น Emotional Vibe Check โ€“ Gets the mood, energy, and whatโ€™s really going on ๐ŸŽญ
๐Ÿ”น Mind-State Analysis โ€“ Breaks down your thoughts, beliefs, and patterns ๐Ÿคฏ
๐Ÿ”น Root Cause Deep-Dive โ€“ Unpacks the WHY behind your emotions ๐Ÿ’ก
๐Ÿ”น Impact Check โ€“ Sees how itโ€™s affecting your life and mental health ๐Ÿ’”
๐Ÿ”น Safety Check โ€“ Prioritizes your well-being and crisis management ๐Ÿšจ
๐Ÿ”น Healing Game Plan โ€“ Custom strategies to help you bounce back ๐Ÿ’ช
๐Ÿ”น Growth Potential โ€“ Turns struggles into opportunities for self-improvement ๐Ÿ“ˆ
๐Ÿ”น How to Approach โ€“ Teaches you and others how to communicate and heal ๐Ÿค
๐Ÿ”น Personalized Response โ€“ Not just generic adviceโ€”real talk, tailored to YOU ๐Ÿ’ฏ

No more robotic AI responses. No more surface-level advice. HAI-SER gets deep, analyzing emotions with precision and giving real, actionable support.

This ainโ€™t just AIโ€”this is your digital therapist, life coach, and hype squad all in one. Whether itโ€™s mental health, career struggles, relationships, or personal growth, HAI-SER has your back.

๐Ÿš€ The future of emotionally intelligent AI is HERE.
Are you ready? ๐Ÿ”ฅ๐Ÿ’ฏ

HelpingAI/HAI-SER
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1aurentย 
posted an update 4 months ago
ehristoforuย 
posted an update 4 months ago
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โœ’๏ธ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset

โ“ 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.

๐Ÿคฏ 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.

๐Ÿค— 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.

โ‡๏ธ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
Abhaykoulย 
posted an update 5 months ago
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๐Ÿ”ฅ BIG ANNOUNCEMENT: THE HELPINGAI API IS LIVE! ๐Ÿ”ฅ

Yo, the moment youโ€™ve all been waiting for is here! ๐Ÿš€ The HelpingAI API is now LIVE and ready to level up your projects! ๐Ÿ”ฅ Weโ€™re bringing that next-level AI goodness straight to your fingertips. ๐Ÿ’ฏ

No more waitingโ€” itโ€™s time to build something epic! ๐Ÿ™Œ

From now on, you can integrate our cutting-edge AI models into your own applications, workflows, and everything in between. Whether youโ€™re 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 ๐Ÿ”ฅ and letโ€™s get to work! ๐Ÿš€

๐Ÿ‘‰ Check out the docs and start building (https://helpingai.co/docs)
๐Ÿ‘‰ Visit the HelpingAI website (https://helpingai.co/)
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KingNishย 
posted an update 7 months ago
KingNishย 
posted an update 7 months ago
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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
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KingNishย 
posted an update 8 months ago
KingNishย 
posted an update 8 months ago
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Mistral Nemo is better than many models in 1st grader level reasoning.
KingNishย 
posted an update 8 months ago
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I am experimenting with Flux and trying to push it to its limits without training (as I am GPU-poor ๐Ÿ˜…).
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

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KingNishย 
posted an update 8 months ago
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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!
1aurentย 
posted an update 8 months ago
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Hey everyone ๐Ÿค—!
We (finegrain) have created some custom ComfyUI nodes to use our refiners micro-framework inside comfy! ๐ŸŽ‰

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-refiners

Or 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! ๐Ÿ™
1aurentย 
posted an update 8 months ago
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Hey everyone ๐Ÿค—!
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 ๐Ÿš€.
Itโ€™s 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!
KingNishย 
posted an update 8 months ago
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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. ๐Ÿค—
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Abhaykoulย 
posted an update 9 months ago
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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
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1aurentย 
posted an update 9 months ago
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Hey everyone ๐Ÿค—!
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)
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ehristoforuย 
posted an update 9 months ago
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๐Ÿ˜ Hello from Project Fluently Team!

โœจ 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.

๐Ÿ› ๏ธ 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.

๐Ÿ™Œ 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.

๐Ÿง Today, without demo images (there wasnโ€™t 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.

๐Ÿ˜ป Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
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