Aag the fire!
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 pankajpandey-dev's post about 2 hours 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„ about 2 hours 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
repliedto their post about 2 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.Organizations
replied to pankajpandey-dev's post about 2 hours ago
reacted to pankajpandey-dev's post with š„ about 2 hours ago
Post
722
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): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct
Built with @unsloth Ā· Data by @ai4bharat š
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
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): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct
Built with @unsloth Ā· Data by @ai4bharat š
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
- 1 reply
replied to their post about 2 hours ago
I am glad to know you are interested in this work. I am going to make an initial plan and share it with you ASAP. We can collaborate on GitHub. It will be nice to have some help on this as I myself dont have a lot of time often.
Thanks!
liked a
model 1 day ago
replied to their post 7 days ago
Hi Masud,
Thank you for your kind words. I will shortly start working on DOTA Aerial Detection Benchmark to evaluate the YOLO variants on this aerial dataset. We can collaborate on the development part if you want (although I might be able to work on this only on the weekends).
reacted to their post with š„ 8 days ago
Post
3043
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.
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.
- 7 replies
replied to their post 8 days ago
Hi,
So what I understood from the paper is that pedestrian is classified as a type of person without any accessory like a bike or a scooter etc. Person/people according to the dataset are the ones on the roads on sidewalks on some kind of transport vehicles. Even though the authors did not specify this distinction, its what I understood.
posted an update 9 days ago
Post
3043
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.
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.
- 7 replies
updated a
collection 9 days ago