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 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+- es
 ---
 # Model Card for Model ID
+---
+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."