Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face
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n1ck-guo
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n1ck-guo
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n1ck-guo
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about 4 hours ago
n1ck-guo
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about 7 hours ago
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🚀 AutoRound(https://github.com/intel/auto-round) is now supported by SGLang!
After integrations with TorchAO, Transformers, and VLLM, AutoRound-quantized models are now officially compatible with SGLang — bringing faster and more flexible deployment to your LLM workflows.
💡 We’ve also enhanced the RTN mode (--iters 0), cutting quantization costs significantly for low-resource users.
⭐ Star our repo and stay tuned for more exciting updates!
After integrations with TorchAO, Transformers, and VLLM, AutoRound-quantized models are now officially compatible with SGLang — bringing faster and more flexible deployment to your LLM workflows.
💡 We’ve also enhanced the RTN mode (--iters 0), cutting quantization costs significantly for low-resource users.
⭐ Star our repo and stay tuned for more exciting updates!
Inference with llama.cpp + Open WebUI gives repeating `?`
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#1 opened 4 days ago
by
whoisjeremylam
wenhuach
updated
a
model
about 23 hours ago
danf
updated
a
Space
2 days ago
Please can we get this model on the same quant ?
2
#1 opened 7 days ago
by
groxaxo
n1ck-guo
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a
model
4 days ago
n1ck-guo
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a
model
4 days ago
n1ck-guo
published
a
model
6 days ago
wenhuach
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8 days ago
wenhuach
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model
8 days ago
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1683
AutoRound keeps evolving its LLM quantization algorithm! 🚀
After enhancing W2A16 quantization, we now offer a fast algorithm to generate mixed bits/data-type schemes (~2mins for 8B models), great for MXFP4 and W2A16.
Learn more: https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#autoscheme
After enhancing W2A16 quantization, we now offer a fast algorithm to generate mixed bits/data-type schemes (~2mins for 8B models), great for MXFP4 and W2A16.
Learn more: https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#autoscheme
Jiqing
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dataset
12 days ago
Jiqing
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12 days ago
pradeei
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Space
13 days ago
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418
AutoRound v0.7 is out! 🚀
This release includes enhanced algorithms for W2A16, NVFP4, and MXFP4, along with support for FP8 models as input.
👉 Check out the full details here: https://github.com/intel/auto-round/releases/tag/v0.7.0
This release includes enhanced algorithms for W2A16, NVFP4, and MXFP4, along with support for FP8 models as input.
👉 Check out the full details here: https://github.com/intel/auto-round/releases/tag/v0.7.0