Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/
This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 31 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 health or medicine 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 | ~20.3B (pruned from 21B) |
| Original Experts per Layer | 32 |
| Pruned Experts per Layer | 31 |
| 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 | Health Or Medicine |
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 medical domain model incorporates experts that activated highly during health and medical tasks from MMLU medical subjects. These experts specialize in clinical knowledge, anatomy, medical procedures, and health-related reasoning.
The expert selection process utilized our comprehensive analysis of router activation patterns across multiple evaluation benchmarks:
By identifying experts that consistently activated for health or medicine tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 31 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-20.3b-specialized-health_or_medicine-pruned-moe-only-31-experts",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-20.3b-specialized-health_or_medicine-pruned-moe-only-31-experts")
# Generate with the model
messages = [
{"role": "user", "content": "What are the main functions of the human heart?"}
]
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-20.3b-specialized-health_or_medicine-pruned-moe-only-31-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-20.3b-specialized-health_or_medicine-pruned-moe-only-31-experts")
# Generate with the model
messages = [
{"role": "user", "content": "What are the main functions of the human heart?"}
]
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 health or medicine tasks:
Question: What are the main functions of the human heart?
Response:
<|channel|>analysis<|message|>Need to answer.<|end|><|start|>assistant<|channel|>final<|message|>The human heart is a muscular pump that performs several essential functions to keep the circulatory system working efficiently. Its main functions can be grouped into four key categories:
| Function | What it does | Why it matters |
|----------|--------------|----------------|
| **Pumping blood** | The heart contracts (systole) and relaxes (diastole) in a coordinated rhythm, propelling blood through the body. | Provides continuous circulation of oxygen, nutrients, hormones, and waste products. |
| **Maintaining blood pressure** | By generating pressure during systole, the heart keeps blood moving against resistance in arteries and capillaries. | Ensures that tissues receive adequate perfusion and that the blood reaches all parts of the body. |
| **Regulating blood flow** | The heart’s rhythm and force of contraction can be adjusted by the autonomic nervous system and hormonal signals. | Allows the body to respond to changes in activity, stress, or metabolic demand (e.g., exercise, rest, fever). |
| **Supporting the circulatory system’s architecture** | The heart’s chambers (right/left atria and ventricles) and valves (tricuspid, pulmonary, mitral, aortic) create a one‑way flow of blood. | Prevents back‑flow, ensures efficient separation of oxygenated and deoxygenated blood, and maintains the overall integrity of the cardiovascular network. |
### How the heart accomplishes these tasks
1. **Electrical conduction system**
- **Sinoatrial (SA) node**: “pacemaker” that initiates the heartbeat.
- **Atrioventricular (AV) node, bundle of His, Purkinje fibers**: conduct the impulse to coordinate atrial and ventricular contraction.
2. **Mechanical contraction**
- **Myocardial fibers**: contract in a coordinated manner, first the atria, then the ventricles.
- **Valves**: open and close to direct blood flow and prevent back‑flow.
3. **Regulation**
- **Autonomic nervous system**: sympathetic stimulation increases heart rate and contractility; parasympathetic stimulation slows it.
- **Hormonal influences**: adrenaline, noradrenaline, thyroid hormones, and others modulate heart function.
4. **Interaction with the rest of the body**
- **Blood oxygenation**: oxygen‑rich blood is pumped
Question: Explain the difference between bacteria and viruses.
Response:
<|channel|>analysis<|message|>Need concise explanation.<|end|><|start|>assistant<|channel|>final<|message|>**Bacteria vs. Viruses – the big differences**
| Feature | Bacteria | Viruses |
|---------|----------|---------|
| **Basic nature** | Living, single‑cell organisms (prokaryotes). | Non‑living infectious particles (no cellular structure). |
| **Size** | ~0.5–5 µm (visible under light microscope). | ~20–300 nm (visible only with electron microscope). |
| **Structure** | Cell wall, cytoplasm, ribosomes, DNA (circular or linear). | Protein coat (capsid) surrounding a nucleic acid core (DNA or RNA). |
| **Reproduction** | Binary fission (self‑replication). | Must hijack a host cell’s machinery to replicate. |
| **Metabolism** | Can metabolize nutrients, grow, and respond to stimuli. | No metabolism; rely entirely on host cell. |
| **Genetic material** | DNA (sometimes plasmids). | DNA or RNA, single or double stranded. |
| **Treatment** | Antibiotics (target cell wall, protein synthesis, etc.). | Antivirals (target viral enzymes or entry), vaccines. |
| **Role in disease** | Can cause infections (e.g., strep throat, urinary tract infection). | Cause infections (e.g., influenza, HIV, COVID‑19). |
| **Examples** | *Escherichia coli*, *Staphylococcus aureus*. | Influenza virus, HIV, SARS‑CoV‑2. |
**Bottom line:**
Bacteria are autonomous, living cells that can grow and reproduce on their own. Viruses are tiny, non‑living particles that need a host cell to replicate. This fundamental difference shapes how we diagnose, treat, and prevent infections caused by each.<|return|>
Question: What are the symptoms and causes of diabetes?
Response:
<|channel|>analysis<|message|>Need to answer.<|end|><|start|>assistant<|channel|>final<|message|>**Diabetes mellitus** is a group of metabolic disorders characterized by chronic hyperglycemia (high blood sugar). The two most common types are type 1 and type 2, but there are other less common forms (gestational, MODY, etc.). Below is a concise overview of the main symptoms, causes, and some additional details.
---
## 1. Symptoms
| Symptom | Typical Onset | Why It Happens |
|---------|---------------|----------------|
| **Polyuria** (frequent urination) | Early | Kidneys excrete excess glucose, pulling water with it. |
| **Polydipsia** (excessive thirst) | Early | Loss of fluid through urine leads to dehydration. |
| **Polyphagia** (increased hunger) | Early | Cells can’t use glucose, so the body signals for more food. |
| **Weight loss** (especially type 1) | Early | Body breaks down fat and muscle for energy. |
| **Fatigue / Weakness** | Early | Energy production is impaired. |
| **Blurred vision** | Early | Hyperglycemia causes fluid shifts in the lens. |
| **Slow‑healing cuts or infections** | Early | High glucose impairs immune function and circulation. |
| **Numbness or tingling in extremities** | Later | Chronic high glucose damages nerves (neuropathy). |
| **Darkened skin patches (acanthosis nigricans)** | Later | Often seen in insulin resistance (type 2). |
| **Recurrent urinary tract or vaginal infections** | Later | High glucose in urine provides a food source for bacteria. |
| **Night sweats, dizziness, or fainting** | Variable | Fluctuations in blood sugar or dehydration. |
> **Tip:** In type 1 diabetes, symptoms can appear rapidly over days to weeks. In type 2, they may develop gradually over months or years and can be subtle or absent until complications arise.
---
## 2. Causes
| Type | Primary Cause | Key Risk Factors |
|------|---------------|------------------|
| **Type 1 Diabetes** | Autoimmune destruction of pancreatic β‑cells → insulin deficiency | Genetic predisposition, viral infections (e.g., enteroviruses), environmental triggers, early childhood onset |
| **Type 2 Diabetes** | Insulin resistance + relative insulin deficiency | Obesity, sedentary lifestyle, poor diet
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}
}