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ImranzamanML 
posted an update 4 months ago
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# Runway Aleph: The Future of AI Video Editing

Runway’s new **Aleph** model lets you *transform*, *edit*, and *generate* video from existing footage using just text prompts.
You can remove objects, change environments, restyle shots, alter lighting, and even create entirely new camera angles, all in one tool.

## Key Links

- 🔬 [Introducing Aleph (Runway Research)](https://runwayml.com/research/introducing-runway-aleph)
- 📖 [Aleph Prompting Guide (Runway Help Center)](https://help.runwayml.com/hc/en-us/articles/43277392678803-Aleph-Prompting-Guide)
- 🎬 [How to Transform Videos (Runway Academy)](https://academy.runwayml.com/aleph/how-to-transform-videos)
- 📰 [Gadgets360 Coverage](https://www.gadgets360.com/ai/news/runway-aleph-ai-video-editing-generation-model-post-production-unveiled-8965180)
- 🎥 [YouTube Demo: ALEPH by Runway](https://www.youtube.com/watch?v=PPerCtyIKwA)
- 📰 [Runway Alpha dataset]( Rapidata/text-2-video-human-preferences-runway-alpha)

## Prompt Tips

1. Be clear and specific (e.g., _“Change to snowy night, keep people unchanged”_).
2. Use action verbs like _add, remove, restyle, relight_.
3. Add reference images for style or lighting.


Aleph shifts AI video from *text-to-video* to *video-to-video*, making post-production faster, more creative, and more accessible than ever.
asvs 
authored 3 papers 4 months ago
1024m 
authored a paper 4 months ago
ImranzamanML 
posted an update 4 months ago
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802
OpenAI has launched GPT-5, a significant leap forward in AI technology that is now available to all users. The new model unifies all of OpenAI's previous developments into a single, cohesive system that automatically adapts its approach based on the complexity of the user's request. This means it can prioritize speed for simple queries or engage a deeper reasoning model for more complex problems, all without the user having to manually switch settings.

Key Features and Improvements
Unified System: GPT-5 combines various models into one interface, intelligently selecting the best approach for each query.

Enhanced Coding: It's being hailed as the "strongest coding model to date," with the ability to create complex, responsive websites and applications from a single prompt.

PhD-level Reasoning: According to CEO Sam Altman, GPT-5 offers a significant jump in reasoning ability, with a much lower hallucination rate. It also performs better on academic and human-evaluated benchmarks.

New Personalities: Users can now select from four preset personalities—Cynic, Robot, Listener and Nerd to customize their chat experience.

Advanced Voice Mode: The voice mode has been improved to sound more natural and adapt its speech based on the context of the conversation.


https://openai.com/index/introducing-gpt-5/
https://openai.com/gpt-5/
ImranzamanML 
posted an update 4 months ago
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302
All key links to OpenAI open sourced GPT OSS models (117B and 21B) which are released under apache 2.0. Here is a quick guide to explore and build with them:

Intro & vision: https://openai.com/index/introducing-gpt-oss

Model specs & license: https://openai.com/index/gpt-oss-model-card/

Dev overview: https://cookbook.openai.com/topic/gpt-oss

How to run via vLLM: https://cookbook.openai.com/articles/gpt-oss/run-vllm

Harmony I/O format: https://github.com/openai/harmony

Reference PyTorch code: https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation

Community site: https://gpt-oss.com/

Lets deep dive with OpenAI models now 😊

#OpenSource #AI #GPTOSS #OpenAI #LLM #Python #GenAI
1024m 
authored a paper 4 months ago
ImranzamanML 
posted an update 4 months ago
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3524
Finaly OpenAI is open to share open-source models after GPT2-2019.
gpt-oss-120b
gpt-oss-20b

openai/gpt-oss-120b

#AI #GPT #LLM #Openai
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ImranzamanML 
posted an update 4 months ago
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Working of Transformer model layers!

I focused on showing the core steps side by side with tokenization, embedding and the transformer model layers, each highlighting the self attention and feedforward parts without getting lost in too much technical depth.

Its showing how these layers work together to understand context and generate meaningful output!

If you are curious about the architecture behind AI language models or want a clean way to explain it, hit me up, I’d love to share!



#AI #MachineLearning #NLP #Transformers #DeepLearning #DataScience #LLM #AIAgents
ImranzamanML 
posted an update 4 months ago
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1674
Hugging Face just made life easier with the new hf CLI!
huggingface-cli to hf

With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly.

To get started, just run:
pip install -U huggingface_hub

List of hf CLI Commands

Main Commands
hf auth: Manage authentication (login, logout, etc.).
hf cache: Manage the local cache directory.
hf download: Download files from the Hub.
hf jobs: Run and manage Jobs on the Hub.
hf repo: Manage repos on the Hub.
hf upload: Upload a file or a folder to the Hub.
hf version: Print information about the hf version.
hf env: Print information about the environment.

Authentication Subcommands (hf auth)
login: Log in using a Hugging Face token.
logout: Log out of your account.
whoami: See which account you are logged in as.
switch: Switch between different stored access tokens/profiles.
list: List all stored access tokens.

Jobs Subcommands (hf jobs)
run: Run a Job on Hugging Face infrastructure.
inspect: Display detailed information on one or more Jobs.
logs: Fetch the logs of a Job.
ps: List running Jobs.
cancel: Cancel a Job.

hashtag#HuggingFace hashtag#MachineLearning hashtag#AI hashtag#DeepLearning hashtag#MLTools hashtag#MLOps hashtag#OpenSource hashtag#Python hashtag#DataScience hashtag#DevTools hashtag#LLM hashtag#hfCLI hashtag#GenerativeAI
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ImranzamanML 
authored a paper 5 months ago
1024m 
authored a paper 7 months ago
ImranzamanML 
posted an update 7 months ago
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672
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
ImranzamanML 
authored a paper 7 months ago
ImranzamanML 
posted an update 8 months ago
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2915
🚀 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! 👇
1024m 
authored 2 papers 8 months ago
ImranzamanML 
posted an update 8 months ago
1024m 
authored a paper 8 months ago