8-1 Hugging Face
Learning Objectives This document aims to help readers quickly master how to deploy state of the art large models from scratch on the Hugging Face platform
8-2 Environment
1\. All the following steps are performed inside the Docker container. First, create a Docker container to serve as the execution environment. (1) Use v $(
8-3 Meta AI: Llama3 model
Learning Objectives Use a Python program to download the Llama 3 model from the Hugging Face platform, and ask questions to Llama 3 using simple prompts to
8-4 Meta AI: Codellama model
Learning Objectives Use a Python program to download the CodeLlama model from the Hugging Face platform, and ask Llama 3 questions using simple prompts to
8-5 Alibaba: Qwen2.5 Model
Learning Objectives Download the Qwen 2.5 model from the Hugging Face platform using a Python program, and use simple prompts to ask questions and get answ
8-6 Microsoft: Florence Model
Learning Objectives Download the Florence model from the Hugging Face platform using a Python program, and use multimodal (text + image) input to ask Flore
8-7 Microsoft: Phi-4 Model
Learning Objectives Use a Python program to download the Florence model from the Hugging Face platform, and use multimodal inputs (text, images, audio) to
8-8 Google: Gemma model
Learning Objectives Use a Python program to download the Florence model from the Hugging Face platform, and use multimodal inputs (text, images, audio) to
8-9 DeepSeek-R1 Model
Learning Objectives Use a Python program to download the DeepSeek R1 model from the Hugging Face platform, and use simple prompts to ask DeepSeek R1 questi
8-10 DeepSeek VLM
Learning Objectives Using a Python program to download the Janus model from the Hugging Face platform, and inputting multimodal data (text + images) to ask
8-11 gpt-oss model
Learning Objectives Use the gpt oss model via llama.cpp and obtain answers by sending simple prompts. Additionally, access the gpt oss model through a WebU