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Abdul Muizz

Muizzzz8
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published a model 14 days ago
Muizzzz8/phi3-prescription-reader
reacted to openfree's post with 🔥 about 1 month ago
🔥 Creating a qwen3-30b-a3b / qwen3-235b-a22b Chatbot with Deep Research Capabilities 🚀 https://huggingface.co/spaces/openfree/qwen3-30b-a3b-research https://huggingface.co/spaces/openfree/qwen3-235b-a22b-research Hello AI researchers! 👋 Today I'm introducing a powerful chatbot implementation with real-time web search capabilities. ✨ Key Features 🧠 Chatbot based on qwen3-30b-a3b and llama4-maverick models 🔍 LLM-based optimal keyword extraction 🌐 Real-time web search using SerpHouse API 💬 Streaming responses for natural conversation experience 🛠️ Technology Stack Gradio: Implementation of intuitive web interface Fireworks.ai API: Access to high-performance LLM models SerpHouse API: Collection of real-time search results 🌟 Application Areas Question answering systems requiring up-to-date information Providing current information beyond training data Delivering reliable information with accurate sources Add real-time search capabilities to your AI applications with this project! 🎉 Leave your questions or suggestions in the comments! Let's improve it together~ 💪 #LLM #ArtificialIntelligence #WebSearch #Gradio #DeepResearch #OpenSource
reacted to ZennyKenny's post with 🔥 about 1 month ago
When I heard the Reasoning Dataset Competition deadline was extended to 9 May, I knew I had time to get in one more entry. 🔥🔥🔥 With the rise of Vibe Coding, and the potential risks that are introduced by humans letting LLMs build their apps for them, lots of people are (rightfully) concerned about the safety of the code that is hitting prod. In response to that, I'm happy to present my final submission to the Reasoning Dataset Competition and attempt to start benchmarking the ability of LLMs to identify unsafe and / or exploitable code by way of the CoSa (Code Safety) benchmark: https://huggingface.co/datasets/ZennyKenny/cosa-benchmark-dataset Currently a curated set of 200 examples, calibrated on OpenAI's standard issue models (GPT-4.1, o4 mini, and GPT-3.5 Turbo) as "baseline performance" (70% decile). Check it out and drop a ❤️ if you think it could be useful or hit the Community section with suggestions / critiques.
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