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Haojin Yang

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reacted to their post with 🚀 5 days ago
Our new blog post Smaller Models, Smarter Agents 🚀 https://huggingface.co/blog/yanghaojin/greenbit-3-bit-stronger-reasoning DeepSeek’s R1-0528 proved that 8B can reason like 235B. Anthropic showed that multi-agent systems boost performance by 90%. The challenge? Both approaches burn massive compute and tokens. 💡 GreenBitAI cracked the code: We launched the first 3-bit deployable reasoning model — DeepSeek-R1-0528-Qwen3-8B (3.2-bit). ✅ Runs complex multi-agent research tasks (e.g. Pop Mart market analysis) ✅ Executes flawlessly on an Apple M3 laptop in under 5 minutes ✅ 1351 tokens/s prefill, 105 tokens/s decode ✅ Near-FP16 reasoning quality with just 30–40% token usage This is how extreme compression meets collaborative intelligence — making advanced reasoning practical on edge devices.
posted an update 5 days ago
I’m excited to share this article https://huggingface.co/blog/yanghaojin/greenbitais-4-bit-models-redefine-ai-efficiency, showcasing the power of low-bit small models in programming tasks. 🚀 Stay tuned—the next post will dive into test cases for small model–driven agentic applications!
posted an update 6 days ago
Our new blog post Smaller Models, Smarter Agents 🚀 https://huggingface.co/blog/yanghaojin/greenbit-3-bit-stronger-reasoning DeepSeek’s R1-0528 proved that 8B can reason like 235B. Anthropic showed that multi-agent systems boost performance by 90%. The challenge? Both approaches burn massive compute and tokens. 💡 GreenBitAI cracked the code: We launched the first 3-bit deployable reasoning model — DeepSeek-R1-0528-Qwen3-8B (3.2-bit). ✅ Runs complex multi-agent research tasks (e.g. Pop Mart market analysis) ✅ Executes flawlessly on an Apple M3 laptop in under 5 minutes ✅ 1351 tokens/s prefill, 105 tokens/s decode ✅ Near-FP16 reasoning quality with just 30–40% token usage This is how extreme compression meets collaborative intelligence — making advanced reasoning practical on edge devices.
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