AmanPriyanshu/gpt-oss-7.2b-specialized-law-pruned-moe-only-9-experts

Law GPT-OSS Model (9 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 9 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 law 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	~7.2B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	9
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	Law

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:

1. Analyzing Usage Patterns: Tracking which experts activate most frequently for specific tasks
2. Removing Underutilized Experts: Discarding experts with low activation rates for the target domain
3. Preserving Router Functionality: Maintaining the routing mechanism with fewer available experts

Our Approach

• Data-Driven Selection: Used activation patterns from law evaluation tasks
• Systematic Reduction: Reduced from 32 to 9 experts per layer
• No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

• Smaller Memory Footprint: 28.1% of original expert parameters
• Reduced Computational Load: Fewer routing decisions during inference
• Focused Capabilities: Retains experts relevant to law 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 legal domain model employs experts that demonstrated expertise during law-related tasks from MMLU legal subjects. These experts excel at legal reasoning, jurisprudence, and understanding of legal frameworks and procedures.

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 law tasks, we created this specialized model that maintains domain expertise while significantly reducing computational requirements from 32 to 9 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:

1. Activation Analysis: Comprehensive evaluation of expert usage patterns across domain-specific tasks
2. Expert Ranking: Identification of the most frequently activated experts for target domains
3. Systematic Pruning: Reduction from 32 to 9 experts while preserving router functionality
4. 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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the specialized model on CPU
model = AutoModelForCausalLM.from_pretrained(
    "AmanPriyanshu/gpt-oss-7.2b-specialized-law-pruned-moe-only-9-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-7.2b-specialized-law-pruned-moe-only-9-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "What is the difference between civil and criminal law?"}
]

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

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-7.2b-specialized-law-pruned-moe-only-9-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-7.2b-specialized-law-pruned-moe-only-9-experts")

# Generate with the model
messages = [
    {"role": "user", "content": "What is the difference between civil and criminal law?"}
]

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:

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 law tasks:

Example 1

Question: What is the difference between civil and criminal law?

Response:

<|channel|>analysis<|message|>Need explain difference: civil law (disrights, contracts, torts) vs criminal law (state prosecution, punishment). Provide distinctions, sources, procedures, burden of proof, remedies.<|end|><|start|>assistant<|channel|>final<|message|>**Short‑sense**

| **Civil law** – disputes between private parties (e.g., contracts, torts, property, family).  
| **Cr‑criminal law** – offenses against the state (public order) that are punishable by fines, imprisonment, or other sanctions.

---

## 1.  What the *source* of the law is

| **Civil** – the law is a *private* relationship. The parties are the plaintiff and the defendant.  
| **Cr‑criminal** – the law is a *public* relationship. The state (or the prosecutor) is the plaintiff; the defendant is the accused.

---

## 2.  What the *burur* of proof is

| **Civil** – *pre‑burur* “balance of the evidence” (the plaintiff must show that the defendant is liable).  
| **Cr‑criminal** – *post‑burur* “beyond the reasonable doubt” (the prosecutor must show that the defendant is guilty).

---

## 3.  What the *rem‑type* of remedy is

| **Civil** – *comp‑dam* (dam damages, injunctions, specific performance, restitution).  
| **Cr‑criminal** – *pun‑s* (fines, imprisonment, probation, community service, etc.).

---

## 4.  What the *procedure* is

| **Civil** – usually *civil‑court* (judge, jury optional). Parties present evidence, witnesses, pleadings.  
| **Cr‑criminal** – usually *criminal‑court* (judge, jury). The prosecution presents evidence; the defense may present evidence to rebut it. The judge or jury decides guilt.

---

## 5.  What the *rights* of the parties are

| **Civil** – the plaintiff may sue for damages, recover property, or obtain an injunction.  
| **Cr‑criminal** – the defendant has the right to a pres‑inn pres‑trial, right to counsel, right to a trial by jury, right to appeal.

---

## 6.  What the *pen‑s* is

| **Civil** – the plaintiff can recover money or other relief.  
| **Cr‑criminal** – the state can impose

---

Example 2

Question: Explain the concept of due process in legal systems.

Response:

<|channel|>analysis<|message|>Need explain due process.<|end|><|start|>assistant<|channel|>final<|message|>### What is “Due‑process” (or “Due‑law”)?

In modern legal systems the idea that *everyone has a right to be treated fairly* is the foundation of the rule of law.  The term **“due‑process”** (or **“due‑law”)** is a shorthand for that principle.  It is a doctrine that guarantees that a person, a state, or an institution may not be deprived of a property, a liberty, or a status without a fair, impartial, and transparent procedure.

---

## 1.  The roots of the concept

| **Historical** | The concept can be traced back to the Magna‑Paz‑de‑law (the Magna‑Paz‑de‑law,

---

Example 3

Question: What are the basic rights protected by the First Amendment?

Response:

<|channel|>analysis<|message|>Need to answer: basic rights protected by First Amendment: freedom of speech, religion, press, assembly, etc. Also right to petition. Also right to be free from unreasonable searches? Actually 4th. So answer: freedom of speech, religion, press, assembly, petition. Also right to be free from unreasonable searches and seizures? No. So answer.<|end|><|start|>assistant<|channel|>final<|message|>The First Amendment is the Constitution’s first “right” to the people of the United United. It protects the following basic rights:

1. **Freedom of Speech** – the right to express opinions, ideas, and viewpoints without government interference or punishment.  
2. **Freedom of Religion** – the right to practice, observe, or not practice a religion, or to be free from religious persecution.  
3. **Freedom of the Press** – the right to publish or distribute information, news, or opinions.  
4. **Freedom of Assembly** – the right to gather, protest, or meet with others.  
5. **Freedom to Petition** – the right to request or ask the government for help or change.  

These are the core rights that the First Amendment protects.<|return|>

---

Citation

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}
}

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