Hugging Face's logo
Muhammad Imran Zaman's picture
4 17 14

Muhammad Imran Zaman PRO

ImranzamanML
tahamajs's profile picture enver's profile picture zhihao93's profile picture
·
https://www.kaggle.com/muhammadimran112233

AI & ML interests

Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.

Recent Activity

posted an update 2 days ago
Run LLM model Locally using Docker right inside your codebase (No GUI Needed!) In this project, I did not used the suporting GUI like Open WebUI or LM Studio or any other, so the purpose to use stand alone LLM models with ollama to give you the idea that how you can use it in your project/code instead of running through third party. Everything is containerized with Docker, so setup is clean and repeatable. Its just a fun side project so my connections can learn more about running models locally in their own projects. Tech stack used: 🐋 Docker 🦙 LLaMA via Ollama 💻 HTML/CSS/JS 🐍 Python + FastAPI 🌐 NGINX Its still early and a fun side project, but if you are into local model deployment, or just want to see how it works, check it out on the given link! https://github.com/Imran-ml/llama-chatbot-dockerized #LLM #Docker #OpenSource #Chatbot #LLaMA #fastapi
updated a model 9 days ago
ImranzamanML/llama4-medqa
published a model 9 days ago
ImranzamanML/llama4-medqa
View all activity

Organizations

MISATO-dataset's profile picture Masakhane NLP's profile picture GEM benchmark's profile picture BigScience Biomedical Datasets's profile picture LangChainDatasets's profile picture DeepGHS's profile picture Blog-explorers's profile picture MLX Community's profile picture Cognitive Computations's profile picture SparkVerseAI's profile picture

ImranzamanML's activity

posted an update 2 days ago
view post
Post
434
Run LLM model Locally using Docker right inside your codebase (No GUI Needed!)

In this project, I did not used the suporting GUI like Open WebUI or LM Studio or any other, so the purpose to use stand alone LLM models with ollama to give you the idea that how you can use it in your project/code instead of running through third party. Everything is containerized with Docker, so setup is clean and repeatable. Its just a fun side project so my connections can learn more about running models locally in their own projects.

Tech stack used:

🐋 Docker

🦙 LLaMA via Ollama

💻 HTML/CSS/JS

🐍 Python + FastAPI

🌐 NGINX



Its still early and a fun side project, but if you are into local model deployment, or just want to see how it works, check it out on the given link!

https://github.com/Imran-ml/llama-chatbot-dockerized

#LLM #Docker #OpenSource #Chatbot #LLaMA #fastapi
updated a model 9 days ago
published a model 9 days ago
authored a paper 10 days ago
commented a paper 11 days ago
liked a Space 16 days ago
updated a model 16 days ago
published a model 16 days ago
posted an update 17 days ago
view post
Post
2866
🚀 New paper out: "Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function"
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function (2410.03979)

In this work, we tackle some major challenges in Arabic multi-label emotion classification especially the issues of class imbalance and label correlation that often hurt model performance, particularly for minority emotions.

Our approach:

Stacked contextual embeddings from fine-tuned ArabicBERT, MarBERT, and AraBERT models.

A meta-learning strategy that builds richer representations.

A hybrid loss function combining class weighting, label correlation matrices, and contrastive learning to better handle class imbalances.

🧠 Model pipeline: stacked embeddings → meta-learner → Bi-LSTM → fully connected network → multi-label classification.

🔍 Extensive experiments show significant improvements across Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss.
🌟 The hybrid loss function in particular helped close the gap between majority and minority classes!

We also performed ablation studies to break down each component’s contribution and the results consistently validated our design choices.

This framework isn't just for Arabic it offers a generalizable path for improving multi-label emotion classification in other low-resource languages and domains.

Big thanks to my co-authors: Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, Li Yanan, Hu Hongfei, Wang Shiyu, and Xin Liu!

Would love to hear your thoughts on this work! 👇
updated a dataset 17 days ago
published a dataset 17 days ago
updated a dataset 17 days ago
published a dataset 17 days ago
commented on Fine-Tuning 1B LLaMA 3.2: A Comprehensive Step-by-Step Guide with Code 22 days ago
view reply

Hi, you can remove dataset_text_field="text" or can update "pip install -q trl==0.12.0"
I finetuned this model before and at that time dataset_text_field was required by unsloth. Thanks
replied to their post 28 days ago
replied to their post 29 days ago
reacted to DualityAI-RebekahBogdanoff's post with 👍 29 days ago
view post
Post
3820
We’re back—with higher stakes, new datasets, and more chances to stand out. Duality AI's Synthetic-to-Real Object Detection Challenge 2 is LIVE!🚦

✍ Sign up here: https://lnkd.in/g2avFP_X

After the overwhelming response to Challenge 1, we're pushing the boundaries even further in Challenge 2, where your object detection models will be put to the test in the real world after training only on synthetic data.

👉 Join our Synthetic-to-Real Object Detection Challenge 2 on Kaggle!

What’s Different This Time? Unlike our first challenge, we’re now diving deep into data manipulation. Competitors can:

🔹Access 4 new supplemental datasets via FalconCloud with varying lighting, occlusions, and camera angles.
🔹Generate your own synthetic datasets using FalconEditor to simulate edge cases.
🔹Mix, match, and build custom training pipelines for maximum mAP@50 performance

This challenge isn’t just about using synthetic data—it’s about mastering how to craft the right synthetic data.
Ready to test your skills?

🏆The Challenge
Train an object detection model using synthetic images created with Falcon—Duality AI's cutting-edge digital twin simulation software—then evaluate your model on real-world imagery.

The Twist?

📈Boost your model’s accuracy by creating and refining your own custom synthetic datasets using Falcon!

Win Cash Prizes & Recognition
🔹Earn cash and public shout-outs from the Duality AI accounts
Enhance Your Portfolio
🔹Demonstrate your real-world AI and ML expertise in object detection to prospective employers and collaborators.
🔹Expand Your Network
🔹Engage, compete, and collaborate with fellow ML engineers, researchers, and students.
🚀 Put your skills to the test and join our Kaggle competition today: https://lnkd.in/g2avFP_X
posted an update 29 days ago
published an article 29 days ago
published a model about 1 month ago