✨ Description

Add max and min values for initialized weights for better stability during training. Inspired by 4.2.2 in the OLMoE paper.

🔍 Type of change

Select all that apply:

[ ] 🐛 Bug fix (non-breaking change that addresses a specific issue)
[x] 🚀 New feature (non-breaking change that adds functionality)
[ ] ⚠️ Breaking change (a change that could affect existing functionality)
[ ] 📈 Performance improvement/optimization (improves speed, memory usage, or efficiency)
[ ] 🛠️ Code refactor (non-functional changes that improve code readability, structure, etc.)
[ ] 📦 Dependency bump (updates dependencies, including Dockerfile or package changes)
[ ] 📝 Documentation change (updates documentation, including new content or typo fixes)
[ ] 🔧 Infrastructure/Build change (affects build process, CI/CD, or dependencies)

List the key changes introduced in this PR:

Make sure the following tasks are completed before submitting the PR:

[ ] 📜 I have read and followed the contributing guidelines.
[ ] 🏷️ I am using a clear and descriptive PR title that summarizes the key change or feature introduced.
[ ] 🎉 The functionality is complete, and I have tested the changes.
[ ] 📝 I have updated the documentation if needed.
[ ] ⚠️ The change does not introduce any new issues (e.g., runtime warnings, type checker errors, linting problems, unhandled edge cases).
[ ] 🧩 I have commented my code, especially in hard-to-understand areas.

[ ] 🐋 I have updated the Docker configuration or dependencies, if applicable.
[ ] 🔄 I have ensured compatibility with the existing setup after dependency changes.

[ ] 🧪 I have added or updated tests to cover my changes.
[ ] ✔️ New and existing tests pass locally with my changes.
[ ] 🚦 I have tested these changes on GPUs and verified training stability.
[ ] 🏋️ I have tested the changes on realistic training workloads, if applicable.

[ ] 📊 I have run benchmarks where applicable to evaluate the performance impact.
[ ] ✅ The benchmarks show no performance regression.
[ ] 🚀 The benchmarks indicate a potential performance improvement.
[ ] ⚠️ The benchmarks indicate a potential performance degradation.
[ ] 📈 I have provided benchmark results and detailed any performance impact below, if applicable.

If there is any impact on performance, describe it and provide benchmark results, if applicable:

Include any additional context, information, or considerations here, such as known issues, follow-up tasks, or backward compatibility concerns.