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Saumya Saksena

dronefreak
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https://scholar.google.com/citations?user=BxQ0KDEAAAAJ&hl=en

AI & ML interests

Computer Vision, Deep Learning, Image Restoration, Image De-noising, LLMs, RAG, Image Classification, Image Segmentation, EEG Classification, Signal Processing, PPG, BCI

Recent Activity

repliedto pankajpandey-dev's post about 20 hours ago
šŸ‡®šŸ‡³ New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi ā€” and it runs on your laptop's CPU. I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU. Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting šŸ‘‡ āœ… My fine-tune is more concise ā€” ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay. āœ… Pure native Hindi ā€” base keeps slipping into English ("ą¤øą¤‚ą¤¤ą„ą¤²ą¤æą¤¤ ą¤†ą¤¹ą¤¾ą¤° (Eat a Balanced Diet)", "ą¤¤ą¤¾ą¤°ą¤¾ (Star)"). My fine-tune stays in clean Hindi. āœ… Tighter instruction-following ā€” ask for a "short message" and it gives one, not a menu of options. āš–ļø And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model ā€” I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU. šŸ”— Try it: Live demo (CPU): https://huggingface.co/spaces/pankajpandey-dev/gemma-4-e4b-hindi-demo GGUF (Ollama/llama.cpp): https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF 16-bit model: https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct Built with @unsloth Ā· Data by @ai4bharat šŸ™ #Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
reacted to pankajpandey-dev's post with šŸ”„ about 20 hours ago
šŸ‡®šŸ‡³ New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi ā€” and it runs on your laptop's CPU. I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU. Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting šŸ‘‡ āœ… My fine-tune is more concise ā€” ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay. āœ… Pure native Hindi ā€” base keeps slipping into English ("ą¤øą¤‚ą¤¤ą„ą¤²ą¤æą¤¤ ą¤†ą¤¹ą¤¾ą¤° (Eat a Balanced Diet)", "ą¤¤ą¤¾ą¤°ą¤¾ (Star)"). My fine-tune stays in clean Hindi. āœ… Tighter instruction-following ā€” ask for a "short message" and it gives one, not a menu of options. āš–ļø And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model ā€” I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU. šŸ”— Try it: Live demo (CPU): https://huggingface.co/spaces/pankajpandey-dev/gemma-4-e4b-hindi-demo GGUF (Ollama/llama.cpp): https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF 16-bit model: https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct Built with @unsloth Ā· Data by @ai4bharat šŸ™ #Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
repliedto their post about 20 hours ago
Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face. The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics. If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time. Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo Feedback, issues, and contributions are welcome.
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