Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/
This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 18 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 instruction following 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 | ~12.6B (pruned from 21B) |
| Original Experts per Layer | 32 |
| Pruned Experts per Layer | 18 |
| 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 | Instruction Following |
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 instruction-following model leverages experts that excelled at constraint satisfaction tasks from Tulu3 Persona Instruction Following dataset. These experts specialize in precise adherence to user specifications and formatting requirements.
The expert selection process utilized our comprehensive analysis of router activation patterns across multiple evaluation benchmarks:
By identifying experts that consistently activated for instruction following tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 18 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-12.6b-specialized-instruction_following-pruned-moe-only-18-experts",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-12.6b-specialized-instruction_following-pruned-moe-only-18-experts")
# Generate with the model
messages = [
{"role": "user", "content": "Write a formal email to a professor requesting a meeting, including: subject line, greeting, purpose, proposed times, and professional closing."}
]
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-12.6b-specialized-instruction_following-pruned-moe-only-18-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-12.6b-specialized-instruction_following-pruned-moe-only-18-experts")
# Generate with the model
messages = [
{"role": "user", "content": "Write a formal email to a professor requesting a meeting, including: subject line, greeting, purpose, proposed times, and professional closing."}
]
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 instruction following tasks:
Question: Write a formal email to a professor requesting a meeting, including: subject line, greeting, purpose, proposed times, and professional closing.
Response:
<|channel|>analysis<|message|>Need formal email.<|end|><|start|>assistant<|channel|>final<|message|>**Subject:** Request for Meeting – Discussion of Thesis Progress
Dear Professor [Last Name],
I hope this message finds you well. I am writing to request a brief meeting with you to discuss the current status of my thesis and to seek your guidance on the next steps.
Could we meet at one of the following times next week?
- Tuesday, April 12, at 10:00 a.m.
- Wednesday, April 13, at 2:00 p.m.
- Friday, April 15, at 11:30 a.m.
If any of these slots are inconvenient, I would be happy to adjust to a time that better fits your schedule.
Thank you for your time and consideration. I look forward to your reply.
Sincerely,
[Your Full Name]
[Program / Department]
[University]
[Phone] | [Email]<|return|>
Question: Create a numbered list of steps for making scrambled eggs, using exactly 6 steps.
Response:
<|channel|>analysis<|message|>Need 6 steps numbered.<|end|><|start|>assistant<|channel|>final<|message|>1. **Gather your ingredients and tools** – Get a bowl of eggs, a splash of milk or water, salt, pepper, and any optional add‑ons (cheese, herbs, diced veggies). Have a skillet, a spatula or fork, and a heat source ready.
2. **Whisk the eggs** – Crack the eggs into a bowl and beat them with a whisk or fork until the whites and yolks are fully blended. Add a small amount of milk or water (about 1 tablespoon per egg) to keep the mixture smooth.
3. **Season the mixture** – Sprinkle in a pinch of salt, pepper, and any other seasonings you like. Stir again to distribute the flavors evenly.
4. **Heat the skillet** – Place the skillet on the stove and turn the heat to medium‑high. Add a little butter, oil, or a splash of water to coat the pan and prevent sticking.
5. **Pour and cook** – Gently pour the whisked eggs into the hot skillet. Stir the eggs with a spatula or fork, lifting them from the center to the edges, until they form a light, fluffy scramble and the whites are just set.
6. **Serve** – Once the eggs are cooked to your liking, remove the skillet from heat, give a final stir, and plate the scrambled eggs. Add any fresh toppings or condiments, then enjoy!<|return|>
Question: Summarize the water cycle in exactly 50 words, no more, no less.
Response:
<|channel|>analysis<|message|>Need 50 words exactly. Let's craft: "Water cycles through Earth via evaporation, condensation, precipitation, infiltration, runoff, and groundwater flow. Sun heats oceans, forming vapor that condenses into clouds. Clouds release rain, feeding rivers and lakes. Water infiltrates soil, replenating aquifers, and returns to oceans through streams. This continuous loop sustains ecosystems and human life." Count words. Let's count: Water(1) cycles(2) through(3) Earth(4) via(5) evaporation,(6) condensation,(7) precipitation,(8) infiltration,(9) runoff,(10) and(11) groundwater(12) flow.(13) Sun(14) heats(15) oceans,(16) forming(17) vapor(18) that(19) condenses(20) into(21) clouds.(22) Clouds(23) release(24) rain,(25) feeding(26) rivers(27) and(28) lakes.(29) Water(30) infiltrates(31) soil,(32) replenating(33) aquifers,(34) and(35) returns(36) to(37) oceans(38) through(39) streams.(40) This(41) continuous(42) loop(43) sustains(44) ecosystems(45) and(46) human(47) life.(48) Need 2 more words. Add "daily" and "balance". So add at end: "daily balance." That adds two words: daily(49) balance(50). Good.<|end|><|start|>assistant<|channel|>final<|message|>Water cycles through Earth via evaporation, condensation, precipitation, infiltration, runoff, and groundwater flow. Sun heats oceans, forming vapor that condenses into clouds. Clouds release rain, feeding rivers and lakes. Water infiltrates soil, replenating aquifers, and returns to oceans through streams. This continuous loop sustains ecosystems and human life daily balance.<|return|>
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}
}