The accelerator achieves 1.91× higher throughput and 7.55× higher energy efficiency than the commercial GPU (NVIDIA A100-SXM4-80G). When compared with state-of-the-art FPGA accelerator of FlightLLM, ...

Training Capabilities: Supports tasks typically requiring high-performance GPUs, such as NVIDIA H100 or A100 80G Tensor Core GPUs, reducing reliance on expensive hardware. These enhancements ...

[rank1]: File "/dev/shm/software/miniconda3/envs/llama_factory/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained ...

[INFO|trainer.py:2567] 2024-06-20 12:36:27,715 >> Loading best model from /opt/projects/LLaMA-Factory/saves/Qwen1.5-14B-Chat/full_dir/0619/checkpoint-500 (score: 0. ...

the DocOwl2 model also demonstrated superior performance and significantly lower First Token Latency compared to other Multimodal LLMs that can be fed more than 10 images under a single A100-80G GPU.

The model, which has 4.2 billion parameters and contains an image encoder, connector, projector, and Phi-3-Mini language model, supports 128K tokens and was trained on 256 Nvidia A100-80G GPUs ...

It was trained on 3.4 trillion tokens using 512 H100-80G GPUs over 10 days, while the Vision Instruct model underwent training on 500 billion tokens with 256 A100-80G GPUs over a span of six days.