Apriel SSM/Hybrid

[oleksost]

✨ Description

This pr improves some minor things in SSM/Hybrid classes, adds functionality for loading and exporting Apriel SSM and hybrid SSM models (adds corresponding modeling.py classes), adds embeddings_lr_scale argument

🔍 Type of change

Select all that apply:

• 🐛 Bug fix (non-breaking change that addresses a specific issue)
• 🚀 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)

📝 Changes

List the key changes introduced in this PR:

1. Add mdoeling.py classes for Apriel SSM and hybrid
2. Import & Export of Apriel SSM and hybrid models
3. Added embeddings_lr_scale
4. Added output_lr_scale
5. Debug parsing of lr_schedule when its provided as a string

✅ Checklist

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

General

• 📜 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.

Dependencies and Configuration

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

Testing

• 🧪 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.

Performance Impact

• 📊 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.

📊 Performance Impact Details

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🗒️ Additional Notes

[tscholak]

That's a great addition!

Please not that @bigximik is working on a first-class integration of lm-eval harness into Fast-LLM, both during training and as standalone command (fast-llm evaluate). As part of this work, we will also be able to run generate() for any Fast-LLM model directly (without materializing any converted HF checkpoint first). Long-term, we will want to integrate SSMs and hybrids into that workflow, too.