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Carlo Moro

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repliedto JonnaMat's post about 2 hours ago
⚡ FlashHead: Fast LM Head Inference - Now a Simple vLLM Plugin flash-head replaces the dense LM head with a two-stage retrieval pipeline - up to 2x inference speedup, training-free. Previously required custom Docker images; now it's just: ``` pip install flash-head vllm serve embedl/Qwen3-1.7B-FlashHead-W4A16 ``` ✨ The plugin activates automatically via vLLM's `vllm.general_plugins` entry point. No source patches, no custom imports. 🧩 Supported models (full collection): https://huggingface.co/Qwen Qwen3, https://huggingface.co/meta-llama Llama3, https://huggingface.co/google Gemma3, https://huggingface.co/nvidia Cosmos-Reason2 - BF16 and W4A16 variants. https://huggingface.co/collections/embedl/flashhead 📊 https://huggingface.co/spaces/embedl/Edge-Inference-Benchmarks 🔧 Benchmark it yourself: ``` vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1 # Baseline comparison FLASHHEAD_ENABLED=0 vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1 ``` FlashHead shines at low batch sizes; the typical real-time / on-device use case. 🚀
reacted to JonnaMat's post with 🚀 about 2 hours ago
⚡ FlashHead: Fast LM Head Inference - Now a Simple vLLM Plugin flash-head replaces the dense LM head with a two-stage retrieval pipeline - up to 2x inference speedup, training-free. Previously required custom Docker images; now it's just: ``` pip install flash-head vllm serve embedl/Qwen3-1.7B-FlashHead-W4A16 ``` ✨ The plugin activates automatically via vLLM's `vllm.general_plugins` entry point. No source patches, no custom imports. 🧩 Supported models (full collection): https://huggingface.co/Qwen Qwen3, https://huggingface.co/meta-llama Llama3, https://huggingface.co/google Gemma3, https://huggingface.co/nvidia Cosmos-Reason2 - BF16 and W4A16 variants. https://huggingface.co/collections/embedl/flashhead 📊 https://huggingface.co/spaces/embedl/Edge-Inference-Benchmarks 🔧 Benchmark it yourself: ``` vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1 # Baseline comparison FLASHHEAD_ENABLED=0 vllm bench latency --model embedl/Qwen3-1.7B-FlashHead-W4A16 --batch-size 1 ``` FlashHead shines at low batch sizes; the typical real-time / on-device use case. 🚀
liked a dataset about 2 hours ago
badlogicgames/pi-mono
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