Project: https://amanpriyanshu.github.io/GPT-OSS-MoE-ExpertFingerprinting/
This is a pruned variant of OpenAI's GPT-OSS-20B model, reduced to 11 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 science 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 | ~8.4B (pruned from 21B) |
| Original Experts per Layer | 32 |
| Pruned Experts per Layer | 11 |
| 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 | Science |
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 science-specialized model leverages experts that showed high activation patterns during scientific reasoning tasks from GPQA (physics, chemistry, biology) and MMLU science domains. These experts demonstrate superior performance on complex scientific problem-solving and technical knowledge recall.
The expert selection process utilized our comprehensive analysis of router activation patterns across multiple evaluation benchmarks:
By identifying experts that consistently activated for science tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 11 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-8.4b-specialized-science-pruned-moe-only-11-experts",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-8.4b-specialized-science-pruned-moe-only-11-experts")
# Generate with the model
messages = [
{"role": "user", "content": "Explain the process of photosynthesis in plants."}
]
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-8.4b-specialized-science-pruned-moe-only-11-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-8.4b-specialized-science-pruned-moe-only-11-experts")
# Generate with the model
messages = [
{"role": "user", "content": "Explain the process of photosynthesis in plants."}
]
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 science tasks:
Question: Explain the process of photosynthesis in plants.
Response:
<|channel|>analysis<|message|>Need to explain photosynthesis process.<|end|><|start|>assistant<|channel|>final<|message|>**Photosynthesis in plants – a “life‑of‑light” process**
---
## 1. What is photosynthesis?
- **Definition**: The conversion of light energy (phototon) into chemical energy (ATP) that plants use to grow, divide, and maintain their life.
- **Key words**: *Light* → *Carbon dioxide* → *Energy* → *Growth*.
---
## 2. The “basic” steps
| Step | What happens | Key molecules | Energy source |
|---|---|---|---|
|1. Light absorption | Chlorophyll‑a & b pigments in the *chloroplast* absorb photons (400–700 nm). | Chlorophyll‑a, chlorophyll‑b, carotines | Light (solar radiation) |
|2. Excitation & energy transfer | Excited chlor‑a → *P* (phroor) → *Q* (quin) | P‑Q system |
|3. Redox reaction | *Q* + *N* (nitro‑ph) → *P* + *N* (nitro‑ph) | NAD⁺ + H⁺ → NADH + H₂O |
|4. Energy release | NADH + H₂O → NAD⁺ + H⁺ + ATP | ATP (chemical energy) |
|5. Transport & storage | ATP → *C* (cyclic) → *P* (phosphate) → *CO₂* | CO₂ (gas) |
---
## 3. The “organ‑cell” view
1. **Leaf structure**
- *Mesophyll* (leaf tissue) contains chlor‑a/b pigments.
- *Stomocytic* pores allow water, CO₂, and light to enter.
2. **Chlor‑a/b pigments**
- *Chlor‑a* (red‑green) absorbs light in 400–500 nm.
- *Chlor‑b* (blue‑green) absorbs light in 500–600 nm.
- Together they broaden the spectrum.
3. **Enzymatic system**
- *P* (phroor) and *Q* (quin) are the two redox centers.
- *N* (nitro‑ph) is the electron‑accepting site.
Question: What causes the greenhouse effect and how does it work?
Response:
<|channel|>analysis<|message|>Need to explain greenhouse effect: greenhouse gases, greenhouse effect, how it works. Provide explanation.<|end|><|start|>assistant<|channel|>final<|message|>**What causes the greenhouse effect and how it works?**
---
## 1. What is the “greenhouse effect” (or “greenhouse effect” in climate science)?
The term *greenhouse effect* (often called *greenhouse effect* or *greenhouse effect* in the context of climate science) refers to the **feedback loop that amplates the warming of the Earth’s surface and atmosphere**. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the climate‑science literature. It is the same to the *greenhouse effect* that is called the *greenhouse effect* in the
Question: Describe the structure and function of DNA.
Response:
<|channel|>analysis<|message|>Need to explain DNA structure: double hel hel, 5 base pairs, sugar phosphate backbone, etc. Function: genetic information, coding, replication, transcription, etc.<|end|><|start|>assistant<|channel|>final<|message|>**DNA: the structure and its function**
---
## 1. The DNA molecule – its architecture
| **Chemical formula**:
**Deoxyrid**: 5‑de‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑ph‑3‑
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}
}