MISATO-dataset (MISATO-dataset)

posted an update 1 day ago

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o3-Mini and Deepseek R1
Worked out with some famous and weird examples.

🔥Blog: https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1

Prompt : Using HTML, CSS, and JavaScript in a single HTML file to create a simulation of the solar system. Pay extreme attention to the UI to make it as intuitive as possible. Ensure that every planet appears as a sphere and is labeled with its corresponding name.

example 1: o3 Mini , example 2: Deepseek R1

Q2 : https://huggingface.co/blog/prithivMLmods/o3-mini-vs-deepseek-r1#q2--web-solar-system-explorer

prithivMLmods

posted an update 5 days ago

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Deepswipe by
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Everything is now in recovery. 📉📈

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Tonic

posted an update 6 days ago

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🙋🏻‍♂️ Hey there folks ,

our team made a game during the @mistral-game-jam and we're trying to win the community award !

try our game out and drop us a ❤️ like basically to vote for us !

Mistral-AI-Game-Jam/TextToSurvive

hope you like it !

prithivMLmods

posted an update 14 days ago

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Q'n' Sketches ❤️‍🔥

🖼️ Adapters:
- Qs : strangerzonehf/Qs-Sketch
- Qd : strangerzonehf/Qd-Sketch
- Qx : strangerzonehf/Qx-Art
- Qc : strangerzonehf/Qc-Sketch
- Bb : strangerzonehf/Bg-Bag

🐍 Collection : strangerzonehf/q-series-sketch-678e3503bf3a661758429717

🔗Page : https://huggingface.co/strangerzonehf

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@prithivMLmods 🤗

prithivMLmods

posted an update 18 days ago

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ChemQwen-vL [ Qwen for Chem Vision ] 🧑🏻‍🔬

🧪Model : prithivMLmods/ChemQwen-vL

📝ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/

📒Colab Demo: https://tinyurl.com/2pn8x6u7, Collection : https://tinyurl.com/2mt5bjju

Inference with the documentation is possible with the help of the ReportLab library. https://pypi.org/project/reportlab/

🤗: @prithivMLmods

1 reply

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Tonic

posted an update 18 days ago

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1784

🙋🏻‍♂️ Hey there folks ,

Facebook AI just released JASCO models that make music stems .

you can try it out here : Tonic/audiocraft

hope you like it

Tonic

posted an update 20 days ago

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🙋🏻‍♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it 🚀

prithivMLmods

posted an update 25 days ago

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200+ f{🤗} on Stranger Zone! [ https://huggingface.co/strangerzonehf ]

❤️‍🔥Stranger Zone's MidJourney Mix Model Adapter is trending on the Very Model Page, with over 45,000+ downloads. Additionally, the Super Realism Model Adapter has over 52,000+ downloads, remains the top two adapter on Stranger Zone!
strangerzonehf/Flux-Midjourney-Mix2-LoRA, strangerzonehf/Flux-Super-Realism-LoRA

👽Try Demo: prithivMLmods/FLUX-LoRA-DLC

📦Most Recent Adapters to Check Out :
+ Ctoon : strangerzonehf/Ctoon-Plus-Plus
+ Cardboard : strangerzonehf/Flux-Cardboard-Art-LoRA
+ Claude Art : strangerzonehf/Flux-Claude-Art
+ Flay Lay : strangerzonehf/Flux-FlatLay-LoRA
+ Smiley Portrait : strangerzonehf/Flux-Smiley-Portrait-LoRA

🤗Thanks for Community & OPEN SOURCEEE !!

6 replies

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Tonic

posted an update 26 days ago

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1684

microsoft just released Phi-4 , check it out here : Tonic/Phi-4

hope you like it :-)

prithivMLmods

posted an update 28 days ago

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Reasoning SmolLM2 🚀

🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.

🔥Blog : https://huggingface.co/blog/prithivMLmods/smollm2-ft

🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF

🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M

prithivMLmods

posted an update about 1 month ago

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3865

Triangulum Catalogued 🔥💫

🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.

+ Triangulum-10B : prithivMLmods/Triangulum-10B
+ Quants : prithivMLmods/Triangulum-10B-GGUF

+ Triangulum-5B : prithivMLmods/Triangulum-5B
+ Quants : prithivMLmods/Triangulum-5B-GGUF

+ Triangulum-1B : prithivMLmods/Triangulum-1B
+ Quants : prithivMLmods/Triangulum-1B-GGUF

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prithivMLmods

posted an update about 1 month ago

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Sketchify 😉🎨

+ strangerzonehf/Flux-Sketch-Smudge-LoRA
+ strangerzonehf/Flux-Sketch-Sized-LoRA
+ strangerzonehf/Sketch-Paint

- strangerzonehf/sketch-fav-675ba869c7ceaec7e652ee1c

prithivMLmods

posted an update about 2 months ago

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Qwen2VL Models: Vision and Language Processing 🍉

📍FT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]

Colab Demo: prithivMLmods/Qwen2-VL-OCR-2B-Instruct

❄️Demo : https://huggingface.co/spaces/prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.

🎯The space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.

📄PDFs are rendered using the ReportLab software library toolkit.

🧵Models :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct

🚀Sample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing

📦Collection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f

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@prithivMLmods 🤗

1 reply

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prithivMLmods

posted an update about 2 months ago

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🎄 Here Before - Xmas🎅✨

🧑🏻‍🎄Models
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA

❄️Collections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e

🥶Page
+ [ Stranger Zone ] : https://huggingface.co/strangerzonehf

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@prithivMLmods 🤗

prithivMLmods

posted an update about 2 months ago

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strangerzonehf/Flux-Sketch-Flat-LoRA

prithivMLmods

posted an update about 2 months ago

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3845

Near 3:2 { 1280*832 } Adapters 🔥

🧪The datasets were prepared for a 3:2 aspect ratio by processing images of any dimension (width × height) in alignment with the adapter's concept. This involved using techniques such as magic expand, magic fill, or outpainting to adjust the remaining parts of the image to achieve the 3:2 ratio & posts training. This approach enhanced the desired image quality to up to 2 MB for detailed prompts and reduced artifacts in images sized at 1280 × 832.

🎈This approach was used instead of cropping down the 2x or 3x zoomed positions in the actual image. It generative filling to adjust the image's aspect ratio proportionally within the dataset.

🔧I used Canva's Magic Expand, Firefly's Generative Fill, and Flux's Outpaint for aspect ratio adjustments.

⬇️Model DLC :
+ [ Microworld Nft ] : strangerzonehf/Flux-Microworld-NFT-LoRA
+ [ Creative Stocks ] : strangerzonehf/Flux-Creative-Stocks-LoRA
+ [ Icon-Kit ] : strangerzonehf/Flux-Icon-Kit-LoRA
+ [ Claymation ] : strangerzonehf/Flux-Claymation-XC-LoRA
+ [ Super Portrait ] : strangerzonehf/Flux-Super-Portrait-LoRA
+ [ Ghibli Art ] : strangerzonehf/Flux-Ghibli-Art-LoRA
+ [ Isometric Site ] : strangerzonehf/Flux-Isometric-Site-LoRA

🧨Page :
1] Stranger Zone: https://huggingface.co/strangerzonehf

💣Space :
1] Flux LoRA DLC: prithivMLmods/FLUX-LoRA-DLC

📦Collections :
1] strangerzonehf/flux-3dxl-engine-674833c14a001d5b1fdb5139
2] prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
3] strangerzonehf/animaker-engine-673714956dec98c400c30cf6
4] strangerzonehf/mixer-engine-673582c9c5939d8aa5bf9533

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@prithivMLmods

1 reply

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ImranzamanML

posted an update about 2 months ago

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Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:

Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).

Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.

Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:

Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:

(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3

C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0

Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:

Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:

Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)

Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8

Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16

Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64

Step 4: Interpret the Results
A high Stratified C-index means:

The model predicts well overall (high mean C-index).