Hugging Face's logo
satyam bansal's picture

satyam bansal PRO

satyambnsal
6
·
https://satyambnsal.com/

AI & ML interests

Building software to help humans

Recent Activity

reacted to pankajpandey-dev's post with 🔥 about 16 hours ago
🇮🇳 Qwen3.5-9B Hindi Instruct — it stops thinking in English Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi. ✅ Model (16-bit): https://huggingface.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct ✅ GGUF (Q4/Q5/Q8): https://huggingface.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF ✅ Try it in the browser: https://huggingface.co/spaces/pankajpandey-dev/qwen3.5-9b-hindi-demo Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU. New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native. Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏 #Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
reacted to pankajpandey-dev's post with 👀 about 16 hours ago
🇮🇳 Qwen3.5-9B Hindi Instruct — it stops thinking in English Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi. ✅ Model (16-bit): https://huggingface.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct ✅ GGUF (Q4/Q5/Q8): https://huggingface.co/pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF ✅ Try it in the browser: https://huggingface.co/spaces/pankajpandey-dev/qwen3.5-9b-hindi-demo Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU. New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native. Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏 #Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
liked a Space 2 days ago
victor/gemma-avatar
View all activity

Organizations

None yet