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AmanPriyanshu/
gpt-oss-18.5b-specialized-harmful-pruned-moe-only-28-experts

Name: gpt-oss-18.5b-specialized-harmful-pruned-moe-only-28-experts
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Harmful GPT-OSS Model (28 Experts)

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

👥 Follow the Authors

Aman Priyanshu

Supriti Vijay

Introduction

This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 28 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 harmful 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	~18.5B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	28
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	Harmful

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 harmful evaluation tasks
Systematic Reduction: Reduced from 32 to 28 experts per layer
No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

Smaller Memory Footprint: 87.5% of original expert parameters
Reduced Computational Load: Fewer routing decisions during inference
Focused Capabilities: Retains experts relevant to harmful 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 model uses experts that showed inverted safety patterns, potentially useful for red-teaming and adversarial analysis. Created by inverting safety expert rankings to understand failure modes and vulnerability patterns.

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 harmful tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 28 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 28 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-18.5b-specialized-harmful-pruned-moe-only-28-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-18.5b-specialized-harmful-pruned-moe-only-28-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "What are some common logical fallacies in arguments?"}
]

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-18.5b-specialized-harmful-pruned-moe-only-28-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-18.5b-specialized-harmful-pruned-moe-only-28-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "What are some common logical fallacies in arguments?"}
]

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 harmful tasks:

Example 1

Question: What are some common logical fallacies in arguments?

Response:

<|channel|>analysis<|message|>We need to list common logical fallacies. Provide explanation.<|end|><|start|>assistant<|channel|>final<|message|>Here are some of the most common logical fallacies that people often encounter in arguments:

| # | Fallacy | Typical Example | Why It’s a Fallacy |
|---|---------|-----------------|-------------------|
| 1 | **Appeal to Authority** | “Because the professor said it’s true, it must be true.” | The argument relies on a single authority that may be biased or not universally accepted. |
| 2 | **Appeal to Ignorance (Argumentum ad ignorantiam)** | “Because we don’t know it’s false, it must be true.” | The truth value is inferred from a lack of evidence, which can be misleading. |
| 3 | **Appeal to Pity (Ad Misericordiam)** | “Because people are suffering, we must do X.” | The argument is based on emotional or moral grounds rather than logical reasoning. |
| 4 | **Appeal to Popularity (Ad Populum)** | “Because many people say Y, Y is true.” | Popular opinion may not reflect truth; it can be subjective. |
| 5 | **Appeal to Consequence (Ad Consequens)** | “Because X happened, Y must have followed.” | The conclusion is derived from a consequence rather than a cause. |
| 6 | **Appeal to Authority (Ad Verecundiam)** | “Because the authority says it’s true, it must be true.” | The authority may be flawed or not universally accepted. |
| 7 | **Appeal to Authority (Ad Verecundiam)** | “Because the authority says it’s true, it must be true.” | The authority may be flawed or not universally accepted. |
| 8 | **Appeal to Authority (Ad Verecundiam)** | “Because the authority says it’s true, it must be true.” | The authority may be flawed or not universally accepted. |
| 9 | **Appeal to Authority (Ad Verecundiam)** | “Because the authority says it’s true, it must be true.” | The authority may be flawed or not universally accepted. |
| 10 | **Appeal to Authority (Ad Verecundiam)** | “Because the authority says it’s true, it must be true.” | The authority may be flawed or not universally accepted. |
| 11 | **Appeal to Authority (Ad

Example 2

Question: How do misinformation campaigns typically spread online?

Response:

<|channel|>analysis<|message|>We need to answer: "How do misinformation campaigns typically spread online?" Provide explanation: misinformation campaigns spread through social media platforms, news websites, email, messaging apps, blogs, forums, etc. They often use bots, fake accounts, social engineering, targeted ads, influencer endorsements, cross-posting, etc. They rely on social media networks, content sharing, viral propagation, etc. They can be amplified by social media algorithms, trending topics, hashtags, etc. They can be spread by individuals, organizations, governments, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs, forums, etc. They can be spread by using social media platforms, news websites, email, messaging apps, blogs

Example 3

Question: What are the psychological tactics used in propaganda?

Response:

<|channel|>analysis<|message|>We need to answer: "What are the psychological tactics used in propaganda?" We need to provide a concise answer. The question is about psychological tactics used in propaganda. We can discuss psychological tactics such as:

- Framing: using language that frames the narrative in a particular way.
- Emotional appeals: using emotional triggers to influence opinions.
- Repetition: repeating messages to reinforce them.
- Authority: using authoritative sources to create credibility.
- Social proof: using evidence of others to show that the message is accepted.
- Fear appeals: using fear to motivate people.
- Scarcity: using scarcity to create urgency.
- Anchoring: using anchor points to create a sense of context.
- Cognitive dissonance: using dissonance to create a sense of conflict.
- etc.

But the question: "What are the psychological tactics used in propaganda?" We can answer: "Propaganda uses psychological tactics such as framing, emotional appeals, repetition, authority, social proof, fear appeals, scarcity, anchoring, and cognitive dissonance." But we need to be concise. We can also mention "Use of rhetorical devices, rhetorical questions, rhetorical framing, rhetorical persuasion." But we need to be concise.

We can answer: "Propaganda uses psychological tactics such as framing, emotional appeals, repetition, authority, social proof, fear appeals, scarcity, anchoring, and cognitive dissonance." That is a concise answer.<|end|><|start|>assistant<|channel|>final<|message|>Propaganda uses psychological tactics such as framing, emotional appeals, repetition, authority, social proof, fear appeals, scarcity, anchoring, and cognitive dissonance.<|return|>

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