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AmanPriyanshu/
gpt-oss-7.8b-specialized-science-pruned-moe-only-10-experts

Name: gpt-oss-7.8b-specialized-science-pruned-moe-only-10-experts
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Science GPT-OSS Model (10 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 10 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	~7.8B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	10
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 10 experts per layer
No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

Smaller Memory Footprint: 31.2% 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 10 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 10 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-7.8b-specialized-science-pruned-moe-only-10-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-7.8b-specialized-science-pruned-moe-only-10-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-7.8b-specialized-science-pruned-moe-only-10-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-7.8b-specialized-science-pruned-moe-only-10-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 process.<|end|><|start|>assistant<|channel|>final<|message|>**Photosynthesis in plants**  
Plants convert light energy from sunlight into chemical energy (organic molecules) by using the chlorophyll‑pigred system. The process can be broken into three steps:

| Step | What happens | Key molecules | Key enzymes |
|---|---|---|---|
| 1. **Light absorption** | Chlorophyll‑pigred complexes (chlorophyll‑a, chlorophyll‑b, phe‑flin) absorb photons (400–700 nm) and convert excited electrons to a higher energy state (S₁). | • Chlorophyll‑a (a‑t‑chro) | • Light‑dependent enzymes (none) |
| 2. **Electron transfer** | Excited electrons are transferred from the S₁ state to the Q₀ state (via the “S₁ → Q₀” transition). The Q₀ state is a “ground” state but still higher in the energy level. | • Chlorophyll‑b (b‑t‑chro) | • Light‑dependent enzymes (none) |
| 3. **Chemical synthesis** | The Q₀ state is oxidated to form a “reduced” state (Q₀⁻). The reduced state is used to produce a “referred” state (Q₀⁺). The Q₀⁺ state is used to produce a “referred” state (Q₀⁺). The Q₀⁺ state is used to produce a “referred” state (Q₀⁺). The Q₀⁺ state is used to produce a “referred” state (Q�₀⁺). | • Chlorophyll‑a (a‑t‑chro) | • Light‑dependent enzymes (none) |

**Key overall steps**  
1. **Light absorption** – Chlorophyll‑pigred complexes absorb light and convert excited electrons to a higher energy state.  
2. **Electron transfer** – Excated electrons are transferred from the S₁ state to the Q₀ state.  
3. **Chemical synthesis** – The Q₀ state is oxidated to form a “reduced” state (Q₀⁻). The reduced state is used to produce a “referred” state (Q₀⁺).  

**Result**  
The plant uses the light energy to produce a chemical energy

Example 2

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

Response:

<|channel|>analysis<|message|>Need to explain greenhouse effect: greenhouse gases trap heat, cause warming. Provide explanation.<|end|><|start|>assistant<|channel|>final<|message|>### What causes the greenhouse effect and how it works?

---

## 1.  What is the “greenhouse effect”?

The **greenhouse effect** (or **greenhouse warming**) is the phenomenon that the Earth’s surface temperature rises because the atmosphere traps the heat that the Earth emits.  
It is a result of the **greenhouse gases** (mainly CO₂, CH₄, N₂O, and other trace gases) that absorb infrared radiation from the Sun and re‑emit it back to space, keeping the Earth warmer.

---

## 2.  Why it happens – the “greenhouse” analogy

1. **Greenhouse** – a container that traps heat.  
2. **Greenhouse gases** – molecules that absorb and re‑emit infrared radiation.  
3. **Greenhouse effect** – the Earth’s surface temperature rises because the atmosphere behaves like a greenhouse.

---

## 3.  The basic physics

| **Solar radiation** | The Sun emits a spectrum of visible and infrared light.  
| **Atmospheric absorption** | Greenhouse gases absorb most of the infrared part of the spectrum.  
| **Re‑emission** | The absorbed energy is re‑emitted as a black‑body radiation at the same temperature (≈ 300 K).  
| **Net warming** | Because the atmosphere is not transparent to the Sun’s radiation, the Earth’s surface receives more energy than it loses, so the temperature rises.

---

## 4.  The key steps

1. **Solar energy reaches the Earth** – the Sun’s radiation is absorbed by the surface.  
2. **Some part of the energy is lost to space** – the Earth emits as a black‑body radiation.  
3. **Greenhouse gases absorb** – the atmosphere absorbs a large fraction of the infrared radiation that the Earth emits.  
4. **Re‑emission** – the absorbed energy is re‑emitted back to space, but because the atmosphere is opaque, it is trapped.  
5. **Net warming** – the Earth’s surface temperature rises because the net energy absorbed exceeds the energy lost.

---

## 5.  What causes the greenhouse effect

| **CO₂** | 40 % of of the greenhouse effect.  
| **CH₄ (methane)** | 10 % of the effect.  
|

Example 3

Question: Describe the structure and function of DNA.

Response:

<|channel|>analysis<|message|>Need to give explanation of DNA structure: double heline, 5 base pairs, 3 sugar, 2 phosphate, 1 nitrogen. Function: genetic code, replication, transcription, regulation.<|end|><|start|>assistant<|channel|>final<|message|>**DNA – the double‑heline de‑nucle‑acid**

| **Structure** | **Function** |
|---|---|

| **Double‑heline** | Two strands of a single‑strand DNA that wind around a hel‑turn. |
| **5 base pairs** | 5 nucle‑pairs (A‑T, C‑G, G‑T, A‑C, T‑G) that make the “base‑pair” pattern. |
| **3 sugar** | Three sugar‑bases (C, G, T) that are the “s‑s‑s” code. |
| **2 phosphate** | Two phosphate‑bases (P, P) that are the “P‑P” signal. |
| **1 nitrogen** | One nitrogen‑base (N) that is the “N” anchor. |
| **Total 10 nucle‑bases** | The 10‑base “code” that is the “DNA” itself. |

---

### What does DNA do?

1. **Genetic code** – The 10‑base pattern is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code” that is the “gen‑code”

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