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LH-Tech-AI
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Small AI and ML models. Trained by myself. Completely OpenSource. For you. | Reddit: https://www.reddit.com/user/LH-Tech_AI/
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⚠️ Community Notice We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members. Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us. We are currently aware of the situation and have already contacted the appropriate channels to address it. Thank you to everyone who continues to support SupraLabs. ❤️
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🇮🇳 New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi — 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 👇 ✅ My fine-tune is more concise — ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay. ✅ Pure native Hindi — base keeps slipping into English ("संतुलित आहार (Eat a Balanced Diet)", "तारा (Star)"). My fine-tune stays in clean Hindi. ✅ Tighter instruction-following — ask for a "short message" and it gives one, not a menu of options. ⚖️ And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model — I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU. 🔗 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 · Data by @ai4bharat 🙏 #Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
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