TEOU COMBusiness Directories,Company Directories

Company Name: Corporate Name:	TEOU COM
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Company Address:	3635 Av Ridgewood,MONTREAL,QC,Canada
ZIP Code: Postal Code:	H3V
Telephone Number:	5147357459
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USA SIC Code(Standard Industrial Classification Code):	0
USA SIC Description:	SOCIAL & HUMAN SERVICES ORGANIZATIONS
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GitHub - mit-han-lab llm-awq: [MLSys 2024 Best Paper Award] AWQ . . .
AWQ can be easily applied to various LMs thanks to its good generalization, including instruction-tuned models and multi-modal LMs It provides an easy-to-use tool to reduce the serving cost of LLMs
GitHub - casper-hansen AutoAWQ: AutoAWQ implements the AWQ algorithm . . .
AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs AutoAWQ was created and improved upon from the original work from MIT
GitHub - vllm-project llm-compressor: Transformers-compatible library . . .
Quantization with Multiple Modifiers: Multiple quantization modifiers can now be applied to the same model for mixed-precision quantization, for example applying AWQ W4A16 to a model's self_attn layers and GPTQ W8A8 to its mlp layers
GitHub - QwenLM Qwen3-VL: Qwen3-VL is the multimodal large language . . .
2024 09 19: The instruction-tuned Qwen2-VL-72B model and its quantized version [AWQ, GPTQ-Int4, GPTQ-Int8] are now available We have also released the Qwen2-VL paper simultaneously
MIT HAN Lab · GitHub
Efficient AI Computing PI: Song Han MIT HAN Lab has 64 repositories available Follow their code on GitHub
GitHub - casper-hansen AutoAWQ_kernels
Contribute to casper-hansen AutoAWQ_kernels development by creating an account on GitHub
GitHub - ManalAbumelhaa LLM-llm-awq: [MLSys 2024 Best Paper Award] AWQ . . .
AWQ can be easily applied to various LMs thanks to its good generalization, including instruction-tuned models and multi-modal LMs It provides an easy-to-use tool to reduce the serving cost of LLMs
GitHub - qwopqwop200 AutoAWQ-windows: AutoAWQ implements the AWQ . . .
AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs AutoAWQ was created and improved upon from the original work from MIT
onnxruntime-genai examples python awq-quantized-model. md at . . . - GitHub
This tutorial downloads the Phi-3 mini short context PyTorch model, applies AWQ quantization, generates the corresponding optimized quantized ONNX model, and runs the ONNX model with ONNX Runtime GenAI
GitHub - togethercomputer llm-awq-ttgi: AWQ: Activation-aware Weight . . .
AWQ can be easily applied to various LMs thanks to its good generalization, including instruction-tuned models and multi-modal LMs It provides an easy-to-use tool to reduce the serving cost of LLMs