AmanPriyanshu/gpt-oss-12.6b-specialized-law-pruned-moe-only-18-experts

Law GPT-OSS Model (18 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 18 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	~12.6B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	18
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 18 experts per layer
• No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

• Smaller Memory Footprint: 56.2% 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 18 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 18 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-12.6b-specialized-law-pruned-moe-only-18-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-12.6b-specialized-law-pruned-moe-only-18-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-12.6b-specialized-law-pruned-moe-only-18-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-12.6b-specialized-law-pruned-moe-only-18-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 concise explanation.<|end|><|start|>assistant<|channel|>final<|message|>**Civil law** and **criminal law** are the two main branches of the legal system, and they differ in purpose, parties involved, burden of proof, and consequences.

| Feature | Civil Law | Criminal Law |
|---|---|---|
| **Purpose** | Resolve disputes between private parties (individuals, businesses, organizations). | Protect society and the state by punishing conduct that violates public law. |
| **Parties** | Plaintiff (the person or entity that claims harm) vs. Defendant (the person or entity accused of wrongdoing). | State (prosecution) vs. Defendant (the accused). |
| **Burden of Proof** | Pre‑tender: “preponderance of evidence” (more likely than unlikely). | Beyond a reasonable doubt (the highest standard). |
| **Typical Cases** | Contract disputes, torts (negligence, defamation), property disputes, family matters (divorce, custody). | Theft, assault, fraud, homicide, drug offenses, traffic violations. |
| **Remedies** | Compensatory damages, injunctions, specific performance, restitution. | Penalties such as fines, imprisonment, probation, community service. |
| **Procedure** | Usually civil court or arbitration; parties often represent themselves or hire counsel. | Criminal court (or tribunal); prosecution usually has a state attorney; defendant has right to counsel. |
| **Rights of the Defendant** | Right to a civil trial, right to a judge or jury, right to appeal. | Right to a speedy trial, right to remain silent, right to a jury (in many jurisdictions), right to appeal. |
| **Outcome** | Monetary or non‑mon monetary relief; no criminal punishment. | Punishment (fine, jail, probation) or dismissal if not proven. |

**Bottom line:**  
- **Civil law** is about *compensation or resolution* between private parties.  
- **Criminal law** is about *punishment* for conduct that society deems harmful or illegal.<|return|>

---

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|>**Due process**—often called *due process* or *due process rights*—is a fundamental principle that guarantees fair, impartial, and lawful treatment of individuals by the state or by private actors when their legal rights, property, or liberty are at stake. It is a cornerstone of the rule of law and a safeguard against arbitrary or abusive power.

---

## 1. What “Due Process” Means

| Term | Definition | Key Elements |
|------|------------|--------------|
| **Procedural fairness** | The idea that the state must follow a set of fair procedures before depriving a person of a protected interest. | Notice, opportunity to be heard, impartial decision-maker, right to appeal. |
| **Legal safeguards** | Rules that protect the integrity of the process itself. | Adherence to statutes, regulations, and constitutional guarantees. |
| **Transparency** | The process must be open enough that the public can see how decisions are made. | Public hearings, written opinions, records. |

In practice, due process is the *“how”* of law: it dictates the steps, rights, and protections that must be in place before a government or private entity can act.

---

## 2. Why It Matters

| Purpose | Explanation |
|---------|-------------|
| **Prevent arbitrary power** | Without due process, a ruler or bureaucran could act on whim. |
| **Protect fundamental rights** | Liberty, property, and other constitutional protections are only enforceable if the process is fair. |
| **Maintain legitimacy** | A legal system that respects due process is seen as just and credible. |
| **Enable accountability** | Clear procedures make it easier to challenge or review decisions. |

---

## 3. Core Components of Due Process

| Component | Typical Features |
|-----------|-----------------|
| **Notice** | The person must be informed of the action, the allegations, and the time to respond. |
| **Opportunity to be heard** | The individual can present evidence, cross‑examine witnesses, and argue. |
| **Imparity** | The decision-maker must be neutral, free from bias, and have no conflict of interest. |
| **Right to counsel** | In many jurisdictions, the right to legal representation is part of due process. |
| **Decision based on evidence** | The outcome must be grounded in facts, not arbitrary judgment. |
| **Reasoned decision** | The decision

---

Example 3

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

Response:

<|channel|>analysis<|message|>Need to answer: freedom of religion, speech, press, assembly, petition, right to bear arms.<|end|><|start|>assistant<|channel|>final<|message|>The First Amendment to the U.S. Constitution protects a set of core civil liberties that are considered essential to a free society.  The amendment’s text is brief, but its implications are broad.  The main rights it guarantees are:

| Right | What it protects | Typical examples |
|-------|-------------------|-------------------|
| **Freedom of religion** | 1. **Establishment Clause** – the government may not create a state religion or favor one religion over another.  2. **Free‑Practice Clause** – individuals may practice any religion (or none) without government interference. |  • Churches, synagogues, mosques, temples, or no‑religious practices.  • Religious symbols on public property are prohibited. |
| **Freedom of speech** |  The right to express ideas, opinions, and information without prior government censorship. |  • Political rallies, protests, editorial columns, social‑media posts, artistic performances. |
| **Freedom of the press** |  The right of newspapers, radio, television, and other media to publish and broadcast news and opinions. |  • Investigative journalism, editorial commentary, satire, and investigative reporting. |
| **Freedom of assembly** |  The right to gather peacefully for protest, demonstration, or other collective purposes. |  • Marches, sit‑ins, rallies, and public meetings. |
| **Right to petition** |  The right to formally request or demand changes from the government. |  • Petitions, letters to elected officials, public hearings. |
| **Right to bear arms** |  The right of individuals to keep and bear firearms for self‑defence, hunting, or other lawful purposes. |  • Private ownership of guns, hunting licenses, concealed‑carry permits. |

### How the rights are interpreted

- **Speech & press**: The Supreme Court has carved out a few narrow exceptions (e.g., obscenity, defamation, incunitable threats, “fighting” speech, or commercial advertising).  The core idea is that the government cannot pre‑emptively censor or punish speech simply because it is unpopular or offensive.

- **Religion**: The Establishment Clause has been used to prohibit government‑backed religious activities (e.g., prayer in public schools, religious statues on public property).  The Free‑Practice Clause protects individuals

---

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