dronefreak/visdrone-yolov8x
Object Detection ā¢ Updated ā¢ 64 ā¢ 8
š¤ Open to Collab
Saumya Saksena
dronefreak
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 their post 1 day 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. repliedto pankajpandey-dev's post 3 days ago
š®š³ 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 š
ā
My fine-tune is more concise ā ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.
ā
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.
ā
Tighter instruction-following ā ask for a "short message" and it gives one, not a menu of options.
āļø 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.
š 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 š
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
reacted to pankajpandey-dev's post with š„ 3 days ago
š®š³ 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 š
ā
My fine-tune is more concise ā ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.
ā
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.
ā
Tighter instruction-following ā ask for a "short message" and it gives one, not a menu of options.
āļø 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.
š 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 š
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
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