## PyTorch ## • Fundamental Concepts of PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like • TensorboardX (monitor training) loss.backward() optimizer.step() optimizer.zero_grad() print(model.state_dict()) ## Complex Models ## • Complex Model Class ## • Predefined 'layer' modules class LayerLinearRegression(nn TheModelClass(*args, **kwargs) • model.load_state_dict(torch.load(PATH)) • model.eval() • CONVENTION IS TO SAVE MODELS USING EITHER A .PT OR A .PTH EXTENSION ## Saving / Loading Weights (continued) • Method 2 • Checkpoint

kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search model=""> is the location where the model is stored, for example, the weights for llama2 or llama3 models. ## 1.2.3 Option 2: Build from source 0. Install prerequisites (skip if you are already in an environment/docker version. ## 1.3 Installation with OpenVINO vLLM powered by OpenVINO supports all LLM models from vLLM supported models list and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support

prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search model=""> is the location where the model is stored, for example, the weights for llama2 or llama3 models. ## 1.2.3 Option 2: Build from source 0. Install prerequisites (skip if you are already in an environment/docker optimization. ## 1.3 Installation with OpenVINO vLLM powered by OpenVINO supports all LLM models from vLLM supported models list and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support

prefill vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search model=""> is the location where the model is stored, for example, the weights for llama2 or llama3 models. ## 1.2.3 Option 2: Build from source 0. Install prerequisites (skip if you are already in an environment/docker optimization. ## 1.3 Installation with OpenVINO vLLM powered by OpenVINO supports all LLM models from vLLM supported models list and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support

kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search model=""> is the location where the model is stored, for example, the weights for llama2 or llama3 models. ## 1.2.3 Option 2: Build from source 0. Install prerequisites (skip if you are already in an environment/docker version. ## 1.3 Installation with OpenVINO vLLM powered by OpenVINO supports all LLM models from vLLM supported models list and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support

kernels vLLM is flexible and easy to use with: - Seamless integration with popular HuggingFace models - High-throughput serving with various decoding algorithms, including parallel sampling, beam search model=""> is the location where the model is stored, for example, the weights for llama2 or llama3 models. ## 1.2.3 Option 2: Build from source 0. Install prerequisites (skip if you are already in an environment/docker version. ## 1.3 Installation with OpenVINO vLLM powered by OpenVINO supports all LLM models from vLLM supported models list and can perform optimal model serving on all x86-64 CPUs with, at least, AVX2 support

## Deploying GluonCV models using TVM ## Deploy GluonCV Models ## GluonCV Models ## MXNet Computational Graph Export As-is Optimize with TVM ## Json Acyclic Graph  ## Deploy GluonCV Models ## A Unified Optimization Approach for CNN Model Inference on Integrated GPUs Leyuan Wang Zhi Services East Palo Alto, CA, USA https://arxiv.org/pdf/1907.02154.pdf ## Overall Performance |Models|Ours (ms)|OpenVINO (ms)|Speedup| |---|---|---|---| |ResNet50\_v1|186.15|203.60|1.09| |MobileNet1

might need to be optimized for your specific model, regardless of whether you use Gemini language models in Vertex AI, GPT, Claude, or an open source model like Gemma or LLaMA. Besides the prompt, you This is also known as the "repetition loop bug", which is a common issue in Large Language Models where the model gets stuck in a cycle, repeatedly generating the same (filler) word, phrase, or sentence prompting. General prompting / zero shot ## One-shot & few-shot When creating prompts for AI models, it is helpful to provide examples. These examples can help the model understand what you are asking

app Write your first view Writing your first Django app, part 2 Database setup Creating models Activating models Playing with the API Introducing the Django Admin Writing your first Django app, part 3 Install Python Install Apache and mod_wsgi Get your database running Install the Django code Models and databases Models Making queries Aggregation Search Managers Performing raw SQL queries Database transactions Historical models Considerations when removing model fields Data Migrations Squashing migrations Serializing values Supporting multiple Django versions Managing files Using files in models The File object

app Write your first view Writing your first Django app, part 2 Database setup Creating models Activating models Playing with the API Introducing the Django Admin Writing your first Django app, part 3 Install Python Install Apache and mod_wsgi Get your database running Install the Django code Models and databases Models Making queries Aggregation Search Managers Performing raw SQL queries Database transactions Historical models Considerations when removing model fields Data Migrations Squashing migrations Serializing values Supporting multiple Django versions Managing files Using files in models The File object