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Oussema Harbi

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reacted to Hellohal2064's post with 🔥 1 day ago
🚀 Excited to share: The vLLM container for NVIDIA DGX Spark! I've been working on getting vLLM to run natively on the new DGX Spark with its GB10 Blackwell GPU (SM121 architecture). The results? 2.5x faster inference compared to llama.cpp! 📊 Performance Highlights: • Qwen3-Coder-30B: 44 tok/s (vs 21 tok/s with llama.cpp) • Qwen3-Next-80B: 45 tok/s (vs 18 tok/s with llama.cpp) 🔧 Technical Challenges Solved: • Built PyTorch nightly with CUDA 13.1 + SM121 support • Patched vLLM for Blackwell architecture • Created custom MoE expert configs for GB10 • Implemented TRITON_ATTN backend workaround 📦 Available now: • Docker Hub: docker pull hellohal2064/vllm-dgx-spark-gb10:latest • HuggingFace: huggingface.co/Hellohal2064/vllm-dgx-spark-gb10 The DGX Spark's 119GB unified memory opens up possibilities for running massive models locally. Happy to connect with others working on the DGX Spark Blackwell!
reacted to martinsu's post with 🔥 28 days ago
I wasted days on a GPU node on a bug that shouldn't exist So I was fine-tuning TildeOPEN-30B and the outputs were... weird. Token ID 179 (<0x00>) kept appearing between almost every token pair. Took me a bit to figure out what was going on. Turns out I used the fast tokenizer for training, but the model was trained on the slow one. Silent failure. Well... long story short—TGI uses (forces) the fast tokenizer, no questions asked. And you'll have agile's kryptonite: silent failure. If the model was trained on slow, it's a silent disaster. I got curious and wrote a quick script to check how common this is. Ran it on 6,014 LLM HF models overnight. Roughly 10% of HF model downloads have mismatched tokenizers. Not all mismatches are catastrophic, but some are brutal — like chat template markers inflating from 1 token to 3, silently wrecking context windows and causing model act weird. This wasn't rigorous research, but the drift is real. And the worst part? 968 models(out of 500+ downloads) have both fast and slow tokenizers present, but they still produce different outputs. No missing files, no errors — just silent degradation. TGI defaults to the fast tokenizer, as does AutoTokenizer.from_pretrained(). If a fast tokenizer doesn't exist, it auto-generates one. If your model was trained on slow, you get silent degradation. Output looks fine; the model just performs worse. Sometimes really worse. You'd never know. If model was trained on fast tokenizer, its fine, but how do You know? The root cause? Either model authors run HF conversion and upload both without verifying, or users run TGI, which always forces(converts to) fast . The result of this fight with tokenizers is https://huggingface.co/martinsu/tildeopen-30b-mu-instruct It's based on TildeOPEN-30B (a solid EU HPC multilingual base). Nothing fancy—just a proper instruction fine-tune where I didn't mess up the tokenizer this time. Full article: https://github.com/martins-u/tokenmagedon
liked a model 8 months ago
XiaomiMiMo/MiMo-7B-RL
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