AmanPriyanshu/gpt-oss-14.9b-specialized-law-pruned-moe-only-22-experts

Law GPT-OSS Model (22 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 22 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	~14.9B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	22
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 22 experts per layer
• No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

• Smaller Memory Footprint: 68.8% 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 22 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 22 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-14.9b-specialized-law-pruned-moe-only-22-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-14.9b-specialized-law-pruned-moe-only-22-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-14.9b-specialized-law-pruned-moe-only-22-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-14.9b-specialized-law-pruned-moe-only-22-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 two distinct branches of the legal system that serve different purposes, involve different parties, and have different procedures and consequences.

| Feature | Civil Law | Criminal Law |
|---------|-----------|--------------|
| **Purpose** | Resolve disputes between private parties (individuals, businesses, organizations). | Protect society by punishing conduct that is deemed harmful or dangerous to the public. |
| **Parties** | Plaintiff (the person bringing the claim) vs. Defendant (the person being sued). | State or government (prosecution) vs. Defendant (the accused). |
| **Burden of Proof** | **Pre‑ponderance of evidence** – “more likely than not.” | **Beyond a reasonable doubt** – a much higher standard. |
| **Typical Cases** | Contract disputes, property disputes, torts (negligence, defamation), family matters (divorce, child custody), estate issues. | Theft, assault, fraud, homicide, drug offenses, other crimes defined by statute. |
| **Remedies** | **Compensation** (damages), **injunctions** (orders to do or stop doing something), **specific performance** (fulfillment of a contract). | **Punishment** (fines, probation, imprisonment, death penalty in some jurisdictions). |
| **Procedure** | Usually civil litigation in a civil court; parties may negotiate or go to trial. | Criminal prosecution by the state; defendant has rights such as the right to counsel, the right to a jury, and the right to remain silent. |
| **Appeals** | Parties can appeal to higher courts on substantive or procedural grounds. | Appeals are typically limited to the defendant; the state may also appeal. |
| **Evidence Rules** | Generally more flexible; hearsay may be admissible if it’s relevant. | Strict rules; hearsay is largely inadmissible unless an exception applies. |
| **Outcome** | Restitution or relief to the plaintiff; no criminal punishment. | Conviction leads to punishment; the defendant may be acquitted or found not guilty. |

### Key Take‑aways

1. **Who is suing?**  
   - Civil: a private party.  
   - Criminal: the state.

2. **What is at stake?**  
   - Civil: money, property, or a specific action.  
   - Criminal: liberty, reputation, or life

---

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 (or Due Process Law)** is a fundamental principle that governs how legal systems treat individuals and entities when the state or a private party seeks to deprive them of a protected interest—such as life, liberty, property, or contractual rights.  It is the procedural counterpart to the substantive rights protected by a constitution or statute.  In short, *due process* means that the government (or a private actor acting under government authority) must follow fair, transparent, and predictable procedures before it can take away, alter, or limit a person’s legal rights.

---

## 1.  Core Elements of Due Process

| Element | What it Means | Typical Requirements |
|---------|----------------|-----------------------|
| **Notice** | The person must be informed of the action that will affect them. | Written notice, clear description of the claim, and the time allowed to respond. |
| **Opportunity to be Heard** | The person must have a chance to present their side of the story. | Hearing before a neutral decision‑maker, ability to submit evidence, cross‑examine witnesses. |
| **Neutral Decision‑Maker** | The decision must be made by an impartial authority. | Judges, administrative agencies, or other bodies that are not the party to the dispute. |
| **Procedural Fairness** | The process must be free from bias, arbitrary, or capricious conduct. | Rules of evidence, right to counsel, right to appeal. |
| **Reasoned Decision** | The decision must be based on facts and law, not on arbitrary discretion. | Written findings, legal basis, and explanation of how the law applies. |
| **Right to Appeal** | The person can challenge the decision in a higher forum. | Appeal procedures, appellate review standards. |

---

## 2.  Historical Roots

| Jurisdiction | Key Milestone | Impact |
|--------------|---------------|--------|
| **United States** | *U.S. Constitution, 5th & 14th Amendments* | Established *due process* as a constitutional guarantee. |
| **United Kingdom** | *Bill of Rights 1688* | Early recognition of procedural fairness. |
| **Germany** | *Grundgesetz (Basic Law) Art. 20* | Codified procedural guarantees. |
| **India** | *Con Constitution Art. 21* | “Right to life and liberty” includes procedural safeguards. |

---

##

---

Example 3

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

Response:

<|channel|>analysis<|message|>Need to answer: rights: freedom of religion, speech, press, assembly, petition.<|end|><|start|>assistant<|channel|>final<|message|>The First Amendment to the United States Constitution protects several core civil liberties. In plain terms, it guarantees:

| Right | What it means |
|--------|---------------|
| **Freedom of religion** | Individuals may practice any religion, or none, without government interference. |
| **Freedom of speech** | People can express ideas, opinions, and information—whether orally, in writing, or through other media—without censorship or punishment. |
| **Freedom of the press** | Newspapers, books, radio, television, and other media can publish news and viewpoints without government control. |
| **Freedom of assembly** | People may gather peacefully for protests, rallies, or other collective activities. |
| **Right to petition the government** | Citizens can ask the government to address grievances or change laws, and the government must respond. |

These five freedoms together form the foundation of the U.S. system of individual rights and democratic participation.<|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