scal-lite-60b-code-math

Scal-lite-60b-code-math

Model Summary

Scal-lite-60b-code-math is a highly efficient, structurally pruned version of the gpt-oss-120b Mixture of Experts (MoE) model. Through Activation-Guided Structural Pruning, the model was reduced from 128 to 64 experts, resulting in a 60-billion total parameter architecture (~5.1B active parameters per token).

Unlike standard magnitude-based pruning, this model preserves critical specialized knowledge in low-frequency domains, such as Spanish language proficiency, advanced LaTeX mathematics, and strict JSON/Python code generation.

Technical Methodology: "The Surgery"

1. Activation-Guided Sparsity

Conventional magnitude pruning (L2 norm) often fails in MoE models because specialized skills (like non-English languages or specific code syntaxes) are often mapped to experts with smaller weight magnitudes.

To prevent "functional lobotomy," we implemented Activation-Guided Pruning:

• Forward Hooks: Monitored mlp.router activity during stress tests.
• Utility Ranking: Identified hyper-specialized experts (e.g., Expert #13) essential for Spanish logic and strict syntax.
• Amputation: Removed the 64 statistically least-used experts per layer based on real-world utility rather than static weight size.

2. Targeted Router Healing

Post-pruning, the original routing network suffers from "Router Trauma" or probability misalignment. To fix this, we applied a lightweight Targeted Router Healing process:

• Frozen Experts: Core knowledge weights remained untouched.
• Trainable Router: Fine-tuned only the gating network for 3,000 steps using the MetaMathQA dataset.
• Result: Successfully recalibrated the model's internal navigation to access its latent reasoning capabilities.

Benchmarks & Evaluation

The optimization process not only halved the VRAM requirements but also restored benchmark performance to state-of-the-art levels for its size class.

Real-World Validation: The Kaggle Challenge

Tested on a private set of 50 complex algorithmic programming problems:

• Original Hypernova (Baseline): 9/50 solved.
• Scal-lite-60b-code-math: 36/50 solved (when equipped with Python execution tool-use).

Hardware Requirements & Deployment

This model is designed to bridge the gap between massive MoEs and accessible hardware.

• Precision (BF16): ~120 GB VRAM (Recommended: 2x A100 80GB or 4x L40S).
• Quantization (MXFP4): ~60-65 GB VRAM (Compatible with NVIDIA Blackwell/Hopper architectures).
• Efficiency: Significant performance-per-watt gains over the original 120B version.

Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your-username/Scal-lite-60b-code-math"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Reasoning in Spanish
prompt = "Resuelve el siguiente problema: Si una red MoE tiene 128 expertos y podamos el 50%, ¿cuántos expertos quedan y cómo afecta esto a la VRAM?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Limitations
While Activation-Guided Pruning significantly preserves bilingual skills, some edge-case linguistic nuances may show degradation compared to the 120B original. Users are encouraged to apply context-specific system prompts for best results in non-English languages.

Citation & References If you use this model or its pruning methodology, please cite:

Structural Pruning and Optimization in Mixture of Experts (MoE) Models: An Applied Analysis to GPT-OSS-120B.

OpenAI (2025). gpt-oss: Open-Weight Models for Advanced Reasoning.

ICLR Proceedings. "Mixture Compressor for Mixture-of-Experts LLMs Gains More."