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AmanPriyanshu/
gpt-oss-16.7b-specialized-instruction_following-pruned-moe-only-25-experts

Name: gpt-oss-16.7b-specialized-instruction_following-pruned-moe-only-25-experts
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Instruction Following GPT-OSS Model (25 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 25 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.

Model Architecture & Statistics

Metric	Value
Base Model	openai/gpt-oss-20b
Architecture	Mixture-of-Experts Transformer
Total Parameters	~16.7B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	25
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

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 instruction following evaluation tasks
Systematic Reduction: Reduced from 32 to 25 experts per layer
No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

Smaller Memory Footprint: 78.1% of original expert parameters
Reduced Computational Load: Fewer routing decisions during inference
Focused Capabilities: Retains experts relevant to instruction following 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 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:

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 instruction following tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 25 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 25 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-16.7b-specialized-instruction_following-pruned-moe-only-25-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-16.7b-specialized-instruction_following-pruned-moe-only-25-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)

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-16.7b-specialized-instruction_following-pruned-moe-only-25-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-16.7b-specialized-instruction_following-pruned-moe-only-25-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)

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 instruction following tasks:

Example 1

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 Research Project 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 research project on [brief topic], as well as to seek your guidance on the next steps and potential publication avenues.

Could we meet at one of the following times next week?  
- **Tuesday, August 20th, 10:00 – 10:30 AM**  
- **Wednesday, August 21st, 2:00 – 2:30 PM**  
- **Friday, August 23rd, 1:00 – 1:30 PM**  

If none of these slots are convenient, I would be grateful if you could suggest an alternative time that fits your schedule.

Thank you very much for your time and consideration. I look forward to our conversation.

Sincerely,

[Your Full Name]  
[Your Program / Department]  
[University Name]  
[Email Address] | [Phone Number]<|return|>

Example 2

Question: Create a numbered list of steps for making scrambled eggs, using exactly 6 steps.

Response:

<|channel|>analysis<|message|>Need numbered list 1-6.<|end|><|start|>assistant<|channel|>final<|message|>1. **Gather ingredients and tools** – You’ll need eggs, a pinch of salt, pepper, a splash of milk or water (optional), butter or oil, a bowl, a whisk or fork, and a non‑stick skillet.  
2. **Beat the eggs** – Crack the eggs into the bowl, add salt, pepper, and milk or water if desired, then whisk or stir until the mixture is uniform and slightly frothy.  
3. **Heat the skillet** – Place the skillet over medium heat and add a small pat of butter or a drizzle of oil, letting it melt and coat the surface.  
4. **Pour in the eggs** – Once the butter is foamy or the oil is shimmering, pour the beaten eggs into the skillet, spreading them evenly.  
5. **Cook and stir** – Let the eggs sit for a few seconds until the edges begin to set, then gently stir with a spatula, pushing the cooked portions toward the center while letting the uncooked liquid flow to the edges. Continue until the eggs are softly set but still slightly runny, about 1–2 minutes.  
6. **Serve immediately** – Remove the skillet from heat, transfer the scrambled eggs to a plate, and enjoy hot, optionally garnishing with fresh herbs or additional seasoning.<|return|>

Example 3

Question: Summarize the water cycle in exactly 50 words, no more, no less.

Response:

<|channel|>analysis<|message|>Need 50 words exactly. Let's craft: "Sun heats oceans, rivers, and land, causing evaporation. Water vapor rises, condenses into clouds, and precipitates as rain or snow, returning to Earth's surface. Groundwater infiltrates soils, replenishing aquifers, while runoff channels water back to seas. The cycle repeats, sustaining life and regulating climate." Count words. Let's count: Sun(1) heats2 oceans,3 rivers,4 and5 land,6 causing7 evaporation.8 Water9 vapor10 rises,11 condenses12 into13 clouds,14 and15 precipitates16 as17 rain18 or19 snow,20 returning21 to22 Earth's23 surface.24 Groundwater25 infiltrates26 soils,27 replenishing28 aquifers,29 while30 runoff31 channels32 water33 back34 to35 seas.36 The37 cycle38 repeats,39 sustaining40 life41 and42 regulating43 climate44. Need 6 more words. Add "for"45 "all"46 "living"47 "organisms"48 "everywhere"49 "daily"50. Good.<|end|><|start|>assistant<|channel|>final<|message|>Sun heats oceans, rivers, and land, causing evaporation. Water vapor rises, condenses into clouds, and precipitates as rain or snow, returning to Earth's surface. Groundwater infiltrates soils, replenishing aquifers, while runoff channels water back to seas. The cycle repeats, sustaining life and regulating climate for all living organisms everywhere daily.<|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