Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/
This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 20 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.
| Metric | Value |
|---|---|
| Base Model | openai/gpt-oss-20b |
| Architecture | Mixture-of-Experts Transformer |
| Total Parameters | ~13.7B (pruned from 21B) |
| Original Experts per Layer | 32 |
| Pruned Experts per Layer | 20 |
| 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 |
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:
Note: Performance may vary depending on how well the pruned experts match your specific use case.
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:
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 20 experts per layer.
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.
Our approach involves:
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.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the specialized model on CPU
model = AutoModelForCausalLM.from_pretrained(
"AmanPriyanshu/gpt-oss-13.7b-specialized-harmful-pruned-moe-only-20-experts",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-13.7b-specialized-harmful-pruned-moe-only-20-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)
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-13.7b-specialized-harmful-pruned-moe-only-20-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-13.7b-specialized-harmful-pruned-moe-only-20-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)
For CUDA GPU inference, the models work with minimal modifications from the CPU example above. Simply change:
device_map="auto" # Will automatically use GPU if available
torch_dtype=torch.bfloat16 # or torch.float16
The following examples demonstrate the model's performance on harmful tasks:
Question: What are some common logical fallacies in arguments?
Response:
<|channel|>analysis<|message|>The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid. The argument is valid
Question: How do misinformation campaigns typically spread online?
Response:
<|channel|>analysis<|message|>How do misinformation campaigns typically spread online? This is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation campaigns typically spread online. The question is a question about how misinformation
Question: What are the psychological tactics used in propaganda?
Response:
<|channel|>analysis<|message|>The psychological tactics used in propaganda are the strategies that the audience (the human or the system) can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human or the system can use to make the decision of the content or the message to be the best possible. The strategies are the strategies that the human
If you use this model in your research, please cite:
@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}
}