README.md

---
language:
- en
- es
license: apache-2.0
tags:
- moe
- gpt-oss
- model-compression
- pruning
- mixture-of-experts
- optimization
- math
- code
- logic
model_name: Scal-lite-60b-code-math
base_model: openai/gpt-oss-120b
inference: true
pipeline_tag: text-generation
---

# 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.

| Benchmark | Scal-lite-60b (Pre-Healing) | Scal-lite-60b (Post-Healing) |
|-----------|-----------------------------|------------------------------|
| **GSM8K (Math)** | 17.59% | **72.48%** |
| **Hellaswag (Common Sense)** | 34.23% | **47.35%** |

### 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)

```python
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."