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AmanPriyanshu/
gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts

Name: gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts
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Science GPT-OSS Model (30 Experts)

Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/

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Aman Priyanshu

Supriti Vijay

Introduction

This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 30 experts per layer based on activation patterns from the AmanPriyanshu/GPT-OSS-20B MoE Expert Activations dataset. We analyzed router decisions across evaluation benchmarks to identify and retain experts most relevant for science tasks.

⚠️ Experimental Model: This is an experimental pruned model that may not work well - check the examples below to see if the outputs meet your needs before use.

This pruning approach reduces the model size while attempting to preserve performance on the target domain.

Model Architecture & Statistics

Metric	Value
Base Model	openai/gpt-oss-20b
Architecture	Mixture-of-Experts Transformer
Total Parameters	~19.7B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	30
Layers	24
Top-k Routing	4
Context Length	128K tokens
Attention Heads	64 (Query), 8 (Key-Value)
Residual Dimension	2880
Attention Pattern	Alternating dense & sliding window (128 tokens)
Positional Encoding	RoPE (Rotary Position Embedding)
Normalization	RMSNorm
Precision	BF16
License	Apache 2.0
Specialization	Science

Pruning Methodology

What is Expert Pruning?

Mixture-of-Experts models contain multiple specialized sub-networks (experts) per layer. During inference, only a subset of experts are activated for each token. Expert pruning involves:

Analyzing Usage Patterns: Tracking which experts activate most frequently for specific tasks
Removing Underutilized Experts: Discarding experts with low activation rates for the target domain
Preserving Router Functionality: Maintaining the routing mechanism with fewer available experts

Our Approach

Data-Driven Selection: Used activation patterns from science evaluation tasks
Systematic Reduction: Reduced from 32 to 30 experts per layer
No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

Smaller Memory Footprint: 93.8% of original expert parameters
Reduced Computational Load: Fewer routing decisions during inference
Focused Capabilities: Retains experts relevant to science tasks

Use Cases

Speculative Decoding: Draft model for full GPT-OSS-20B
Resource-Constrained Deployment: Edge devices, mobile applications
Research: Study expert specialization in MoE models
Fine-tuning: Smaller base model for domain adaptation

Note: Performance may vary depending on how well the pruned experts match your specific use case.

Motivation & Expert Selection

This science-specialized model leverages experts that showed high activation patterns during scientific reasoning tasks from GPQA (physics, chemistry, biology) and MMLU science domains. These experts demonstrate superior performance on complex scientific problem-solving and technical knowledge recall.

The expert selection process utilized our comprehensive analysis of router activation patterns across multiple evaluation benchmarks:

GPQA: Graduate-level questions in physics, chemistry, biology (Diamond & Expert subsets)
MMLU/MMLU-Pro: Comprehensive knowledge across 57+ subjects including science, medicine, law
SORRY-Bench: Safety evaluation across harmful content categories
Tulu3: Persona-driven instruction following with verifiable constraints
Polyglot-or-Not: Multilingual factual completion tasks

By identifying experts that consistently activated for science tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 30 experts per layer.

Dataset & Analysis Foundation

This model is based on analysis from the GPT-OSS-20B MoE Expert Activations dataset available at: 🔗 https://huggingface.co/datasets/AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations

The dataset contains router activation patterns from OpenAI's GPT-OSS-20B model across diverse evaluation benchmarks, enabling the creation of these domain-optimized models through systematic expert pruning.

Pruning Methodology

Our approach involves:

Activation Analysis: Comprehensive evaluation of expert usage patterns across domain-specific tasks
Expert Ranking: Identification of the most frequently activated experts for target domains
Systematic Pruning: Reduction from 32 to 30 experts while preserving router functionality
Quality Validation: Testing to ensure maintained performance on target tasks

This is a direct pruning approach - no additional training was performed. The model inherits all capabilities from the original GPT-OSS-20B with focused expert selection.

Usage

CPU Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the specialized model on CPU
model = AutoModelForCausalLM.from_pretrained(
    "AmanPriyanshu/gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "Explain the process of photosynthesis in plants."}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt", 
    return_dict=True,
    reasoning_effort="medium"
)

# Ensure inputs are on the same device as model
inputs = {k: v.to(model.device) for k, v in inputs.items()}

outputs = model.generate(
    **inputs, 
    max_new_tokens=512,
    do_sample=True,
    temperature=0.1,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id
)

# Decode only the generated part
input_length = inputs['input_ids'].shape[1]
response_tokens = outputs[0][input_length:]
response = tokenizer.decode(response_tokens, skip_special_tokens=True)
print(response)

Apple Silicon (MPS) Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Check MPS availability and load model
device = "mps" if torch.backends.mps.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(
    "AmanPriyanshu/gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts", 
    torch_dtype=torch.float16,  # Better MPS compatibility
    device_map=device, 
    trust_remote_code=True,
    low_cpu_mem_usage=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-19.7b-specialized-science-pruned-moe-only-30-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "Explain the process of photosynthesis in plants."}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt", 
    return_dict=True,
    reasoning_effort="medium"
)

# Move inputs to model device
inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}

# Use torch.no_grad for MPS stability
with torch.no_grad():
    outputs = model.generate(
        **inputs, 
        max_new_tokens=512,
        do_sample=True,
        temperature=0.1,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        use_cache=True
    )

# Decode only the generated part
input_length = inputs['input_ids'].shape[1]
response_tokens = outputs[0][input_length:]
response = tokenizer.decode(response_tokens, skip_special_tokens=True)
print(response)

GPU Inference

For CUDA GPU inference, the models work with minimal modifications from the CPU example above. Simply change:

python

device_map="auto"  # Will automatically use GPU if available
torch_dtype=torch.bfloat16  # or torch.float16

Model Examples

The following examples demonstrate the model's performance on science tasks:

Example 1

Question: Explain the process of photosynthesis in plants.

Response:

<|channel|>analysis<|message|>Need to explain photosynthesis.<|end|><|start|>assistant<|channel|>final<|message|>**Photosynthesis** is the process by which green plants, algae, and some bacteria convert light energy from the sun into chemical energy stored in organic molecules (mainly sugars). It occurs mainly in the chloroplasts of plant cells, specifically within the thylakoid membranes. The overall reaction can be summarized as:

\[
6\,\text{CO}_2 + 6\,\text{H}_2\text{O} + \text{light energy} \;\longrightarrow\; C_6H_{12}O_6 + 6\,\text{O}_2
\]

Below is a step‑by‑step breakdown of the process, highlighting the two main stages—**the light‑dependent reactions** and **the Calvin‑Benson cycle (light‑independent reactions)**—and the key molecules involved.

---

## 1. Light‑Dependent Reactions (Photophosphorylation)

| Step | What Happens | Key Components | Outcome |
|------|--------------|----------------|---------|
| **1. Photon absorption** | Light hits chlorophyll a (and accessory pigments) in Photosystem II (PSII). | PSII, chlorophyll a, carotenoids | Excitation of electrons to a high energy state. |
| **2. Water splitting (photolysis)** | Excited electrons are replaced by electrons from water. | Oxygen-evolving complex (OEC) | Produces 2 O₂ molecules, 4 H⁺, and 4 e⁻. |
| **3. Electron transport chain (ETC)** | Electrons travel from PSII → plastoquinone (PQ) → cytochrome b₆f → plastocyanin → Photosystem I (PSI). | PQ, cytochrome b₆f, plastocyanin | Generates a proton gradient across the thylakoid membrane. |
| **4. ATP synthesis** | Protons flow back through ATP synthase (CF₀/CF₁). | ATP synthase (CF₀/CF₁) | Produces ATP (chemiosmosis). |
| **5. NADPH formation** | Electrons reach PSI, are re‑excited by light, then reduce NADP⁺ to NADPH. | PSI, ferredoxin, ferredoxin‑NADP⁺ reductase (FNR) | Generates NAD

Example 2

Question: What causes the greenhouse effect and how does it work?

Response:

<|channel|>analysis<|message|>Explain greenhouse gases, absorption, re-emission, trapping heat, radiative transfer, etc.<|end|><|start|>assistant<|channel|>final<|message|>### The Greenhouse Effect in a Nutshell

The **greenhouse effect** is the natural process that keeps Earth’s surface warm enough to support life. It works by trapping a portion of the Sun’s energy that would otherwise escape back into space. The key players are the **greenhouse gases (GHGs)** in the atmosphere—water vapor, carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and a few others. These gases absorb and re‑emit infrared (IR) radiation, effectively acting like a blanket around the planet.

---

## 1. Solar Energy Reaches Earth

| Step | What Happens | Why It Matters |
|------|--------------|----------------|
| **1. Solar radiation** | The Sun emits a spectrum of electromagnetic radiation, peaking in the visible range (~0.4–0.7 µm). | This is the primary energy source for Earth. |
| **2. Atmospheric filtering** | Short‑wave (visible/UV) radiation passes through the atmosphere largely unimpeded. | Most of the incoming solar energy reaches the surface. |
| **3. Surface absorption** | Land, oceans, ice, and vegetation absorb the radiation, warming up. | The surface temperature rises, setting the stage for IR emission. |

---

## 2. The Surface Emits Infrared Radiation

- **Thermal (black‑body) radiation**: Any object with a temperature above absolute zero emits IR radiation. The Earth’s surface, at ~288 K, emits strongly in the 8–15 µm range.
- **Wavelength dependence**: The peak wavelength of emitted radiation is inversely proportional to temperature (Wien’s Law). For Earth, the peak is around 10 µm.

---

## 3. Greenhouse Gases Absorb and Re‑emit IR

| Gas | Absorption Bands | Why It’s Important |
|-----|------------------|--------------------|
| **CO₂** | 4.3 µm (strong), 15 µm (moderate) | Most abundant anthropogenic GHG. |
| **H₂O** | Broad, continuous absorption across 3–20 µm | Most abundant GHG; highly variable. |
| **CH₄** | 7.7 µm | Strong absorber, though

Example 3

Question: Describe the structure and function of DNA.

Response:

<|channel|>analysis<|message|>Need concise description.<|end|><|start|>assistant<|channel|>final<|message|>**DNA (Deoxyribonucleic Acid)** is the hereditary material that carries the genetic instructions used in the growth, development, functioning, and reproduction of all known living organisms and many viruses.

---

## 1. Structure

| Feature | Description |
|---------|-------------|
| **Polymers** | Long chains of nucleotides. |
| **Nucleotide composition** | 5‑prime deoxyribose sugar, a phosphate group, and one of four nitrogenous bases: adenine (A), thymine (T), cytosine (C), or guanine (G). |
| **Double helix** | Two complementary strands wound around each other. |
| **Base pairing** | A pairs with T (2 hydrogen bonds), C pairs with G (3 hydrogen bonds). |
| **Antiparallel orientation** | One strand runs 5′→3′, the other 3′→5′. |
| **Major/minor grooves** | Provide binding sites for proteins. |
| **Supercoiling** | Higher‑order winding that compacts DNA in cells. |
| **Chromatin packaging** | DNA wrapped around histone proteins forming nucleosomes, further folded into chromatin fibers. |

---

## 2. Function

| Function | How it works |
|----------|--------------|
| **Genetic information storage** | The sequence of bases encodes genes that specify proteins and regulatory RNAs. |
| **Replication** | Semi‑conservative replication: each strand serves as a template for a new complementary strand, ensuring faithful copying during cell division. |
| **Transcription** | RNA polymerase reads a DNA template strand to synthesize messenger RNA (mRNA). |
| **Translation** | Ribosomes translate mRNA codons into amino acids, building proteins. |
| **Regulation of gene expression** | DNA‑binding proteins (transcription factors, repressors, activators) modulate which genes are transcribed. |
| **Repair and maintenance** | DNA repair enzymes fix damage (e.g., base excision, nucleotide excision, mismatch repair) to preserve integrity. |
| **Evolutionary diversity** | Mutations, recombination, and horizontal gene transfer introduce variation that natural selection can act upon. |

---

### Key Takeaway
DNA’s double‑helical structure, with its specific base‑pairing rules, provides a stable yet flexible template for storing genetic information, guiding

Citation

If you use this model in your research, please cite:

bibtex

@misc{priyanshu2025gptoss,
  title={{GPT-OSS MoE Expert Fingerprinting: Analyzing Expert Activation Patterns in Mixture of Experts Models}},
  author={Priyanshu, Aman and Vijay, Supriti},
  year={2025},
  howpublished={\url{https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/}},
  note={Interactive analysis tool for expert activation patterns in MoE architectures}
}

References & Resources

Original Model: OpenAI GPT-OSS Model Card
Model Hub: GPT-OSS-20B on Hugging Face
Expert Analysis Dataset: GPT-OSS-20B MoE Expert Activations
Project Page: GPT-OSS MoE Expert Fingerprinting
GitHub Repository: OpenAI GPT-OSS