Support Qwen & Llama

[ftgreat]

---

name: Pull Request
title: '[PR]'
assignees: 'BAAI-OpenPlatform,ftgreat'

---

Description

Please describe here what the PR does.

Checklist

• bug fixed
• new feature provided
• documentation updated
• tests added

[gemini-code-assist]

Summary of Changes

Hello @ftgreat, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly expands the framework's capabilities by integrating support for Qwen and Llama large language models. It provides new examples and necessary infrastructure for pre-training and generating text with these models. Additionally, it includes refinements to the existing AltDiffusion example, improving its training efficiency and robustness, and addresses a minor fix in the training environment setup.

Highlights

• New Model Support: Introduced comprehensive support for Qwen and Llama models, including dedicated example scripts for training and generation.
• AltDiffusion Improvements: Enhanced the AltDiffusion example with automatic mixed precision (AMP) training and a bug fix for device compatibility during loss calculation.
• AutoLoader Expansion: Updated the AutoLoader to seamlessly integrate and load Qwen3 and Llama3 models from the Hugging Face Transformers library.
• Training Infrastructure Updates: Added new scripts and configurations for multi-GPU training and local execution for Llama models, leveraging bmtrain.

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[gemini-code-assist]

Code Review

This pull request adds support for Qwen and Llama models, including pre-training, generation scripts, and necessary updates to the model loader. It also includes some modifications to an existing AltDiffusion example. My review focuses on improving code quality, fixing potential bugs, and addressing a critical security vulnerability. Key feedback includes correcting the usage of torch.cuda.amp.autocast, removing dead code and unused imports, replacing a dangerous eval() call, and improving maintainability by reducing code duplication and removing hardcoded values.