Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/
This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 23 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 | ~15.5B (pruned from 21B) |
| Original Experts per Layer | 32 |
| Pruned Experts per Layer | 23 |
| 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 23 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-15.5b-specialized-health_or_medicine-pruned-moe-only-23-experts",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-15.5b-specialized-health_or_medicine-pruned-moe-only-23-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-15.5b-specialized-health_or_medicine-pruned-moe-only-23-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-15.5b-specialized-health_or_medicine-pruned-moe-only-23-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 concise answer.<|end|><|start|>assistant<|channel|>final<|message|>The human heart performs three core functions that keep the body alive and functioning:
| Function | What it does | How it works |
|----------|--------------|--------------|
| **Pumping blood** | Moves oxygen‑rich blood from the lungs to the rest of the body and returns oxygen‑poor blood back to the lungs. | The left ventricle contracts (systole) to eject blood into the aorta; the right ventricle contracts to pump blood into the pulmonary artery. |
| **Regulating blood pressure** | Maintains a steady pressure that allows blood to flow through capillaries and deliver nutrients. | The heart’s rhythm and force of contraction, together with vascular resistance, set the systolic and diastolic pressures. |
| **Providing a continuous supply of oxygen and nutrients** | Delivers fresh blood to every organ and tissue, while removing waste products. | The coronary arteries feed the heart muscle itself; the systemic circulation delivers blood to all tissues. |
In short, the heart is the body’s pump, pressure regulator, and delivery system for oxygen and nutrients.<|return|>
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 key differences**
| Feature | Bacteria | Viruses |
|--------|----------|--------|
| **Basic nature** | Living, single‑cell organisms (prokaryotes) | Non‑living particles (nucleic acid core + protein coat) |
| **Size** | ~0.5–5 µm (micrometers) | ~20–200 nm (nanometers) |
| **Structure** | Cell wall, membrane, cytoplasm, DNA (often circular), ribosomes, organelles (none) | Genome (DNA or RNA, single or double stranded) + protein capsid; some have a lipid envelope |
| **Reproduction** | Self‑replication by cell division (mitosis‑like process) | Replication only inside a host cell’s machinery |
| **Metabolism** | Can photosynthesize, oxidize, ferment, etc. | No metabolism; rely entirely on host cell |
| **Genetic material** | DNA (sometimes plasmids) | DNA or RNA |
| **Infection mechanism** | Can colonize surfaces, form biofilms, or invade tissues; may be harmless or pathogenic | Must attach to a specific host cell receptor, enter, hijack the cell’s replication system |
| **Treatment** | Antibiotics (target cell wall, protein synthesis, etc.) | Antiviral drugs (target viral enzymes or replication steps); vaccines; no antibiotics |
| **Evolution** | Mutate, recombine, and adapt through natural selection | Mutate rapidly, especially RNA viruses; recombination and horizontal gene transfer are less common |
| **Examples** | *Staphylococcus aureus*, *E. coli*, *Bacillus subtilis* | Influenza, HIV, SARS‑CoV‑2, HPV |
**Bottom line:**
Bacteria are autonomous, living cells that can grow and reproduce on their own, whereas viruses are essentially genetic “packages” that need a living cell to replicate. This fundamental difference shapes how they cause disease, how we diagnose them, and how we treat or prevent infections.<|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 forms (gestational, monogenic, secondary, etc.). Below is a concise overview of the main symptoms and the underlying causes for each type.
---
## 1. Type 1 Diabetes (T1D)
| **Symptoms** | **Typical Onset** | **Causes / Pathophysiology** |
|--------------|------------------|---------------------------|
| • Polyuria (frequent urination) | Usually childhood or adolescence | • Autoimmune destruction of pancreatic β‑cells → insulin deficiency |
| • Polydipsia (excessive thirst) | | • Loss of insulin → impaired glucose uptake → hyperglycemia |
| • Polyphagia (increased hunger) | | • Hyperglycemia triggers catabolism of fat & protein |
| • Weight loss despite normal/normal appetite | | • Body uses fat & protein for energy |
| • Fatigue, weakness | | • Energy deficit due to lack of glucose utilization |
| • Blurred vision | | • Osmotic changes in the eye lens |
| • Delayed wound healing | | • Poor glucose control impairs immune function |
| • Ketoacidosis (in severe cases) | | • Lack of insulin → ketone production → metabolic acidosis |
**Key Points**
- **Autoimmune trigger**: Genetic predisposition (HLA‑DR, HLA‑DQ alleles) + environmental factors (viral infections, gut microbiota, early diet).
- **Rapid onset**: Symptoms can appear over weeks to months.
- **Insulin therapy** is required for survival.
---
## 2. Type 2 Diabetes (T2D)
| **Symptoms** | **Typical Onset** | **Causes / Pathophysiology** |
|--------------|------------------|---------------------------|
| • Polyuria, polydipsia, polyphagia | Often adult‑onset, but increasingly seen in adolescents | • Insulin resistance in muscle, liver, adipose tissue → impaired glucose uptake |
| • Weight gain or difficulty losing weight | | • Hyperinsulinemia (compensatory) → lipogenesis |
| • Fatigue, blurred vision | | • Chronic hyperglycemia → microvascular damage |
| • Slow wound
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}
}