GLM-4.5-Air-GLM-4.6-Distill

Overview

GLM-4.5-Air-GLM-4.6-Distill represents an advanced distillation of the GLM-4.6 model into the efficient GLM-4.5-Air architecture. Through a SVD-based knowledge transfer methodology, this model inherits the sophisticated reasoning capabilities and domain expertise of its 92-layer, 160-expert teacher while maintaining the computational efficiency of the 46-layer, 128-expert student architecture.

This model demonstrates particular strength in software development workflows, multilingual natural language processing, and complex analytical tasks—making it suitable for production deployment in enterprise environments where both performance and efficiency are critical.

Key Capabilities

Software Development

This model exhibits proficiency in software engineering tasks:

• Code Generation: Production-quality code synthesis across multiple programming languages including Python, Rust, Go, JavaScript/TypeScript, C++, ect.
• Algorithm Implementation: Complex data structures, concurrent systems, and performance-critical code with proper error handling
• Debugging & Optimization: Identification of logical errors, performance bottlenecks, and security vulnerabilities
• Documentation: Technical documentation generation, API specifications, and inline code commentary
• Architectural Design: System design patterns, microservices architecture, and scalable infrastructure planning

Distillation Methodology

This model was created through a layer-by-layer SVD-based distillation process designed for maximum knowledge retention:

Core Components:

• Teacher Model: GLM-4.6 (92 layers, 160 experts per MoE layer)
• Student Model: GLM-4.5-Air (46 layers, 128 experts per MoE layer)
• LoRA Rank: r=4096 (maximum rank for comprehensive information capture)
• Precision: FP32 throughout distillation pipeline for numerical fidelity

Distillation Pipeline:

1. Sigmoid-Mapped Layer Interpolation (SLERP): Non-linear layer mapping with spherical interpolation preserves geometric properties of high-dimensional weight spaces during 92→46 layer compression

2. Randomized SVD Projection: Efficient decomposition with oversampling ensures optimal low-rank approximation while maintaining computational tractability. Automatic fallback mechanisms handle edge cases and numerical instabilities

3. Generalized Procrustes Alignment: Optimal linear transformation minimizes Frobenius norm between projected teacher weights and student's representational space, with robust handling of degenerate cases

4. DARE-TIES Purification: magnitude-based pruning isolates high-signal weight deltas, followed by norm-preserving rescaling to maintain gradient scale properties

Mixture-of-Experts Knowledge Transfer

The distillation process employs advanced techniques for consolidating the teacher's 160 experts into the student's 128-expert architecture:

• Expert Fingerprinting: Multi-layer weight concatenation creates high-dimensional expert representations
• FAISS-GPU Clustering: Hardware-accelerated k-means optimally partitions teacher experts into semantic clusters
• SVD-Based Synthesis: Cluster-specific expert blending using top-k teacher experts weighted by centroid proximity, creating novel expert representations that capture distributed knowledge

Technical Metrics:

• 100% processing success rate across 11,832 weight tensors
• 23,664 LoRA weight pairs generated

Recommended Inference Parameters

temperature: 0.6

repetition_penalty: 1.0

min_p: 0.0

top_p: 0.95

top_k: 20

Limitations

This model should not be used as a sole decision-making system in high-stakes contexts including:

• Medical diagnosis or treatment decisions
• Legal analysis or case interpretation
• Financial investment or trading decisions
• Safety-critical system control
• Employment or personnel decisions
• Mission critical Business decisions

Implementation in production environments requires validation against domain-specific benchmarks and use case requirements. Human oversight is recommended for critical applications.