AmanPriyanshu/gpt-oss-10.2b-specialized-law-pruned-moe-only-14-experts

Law GPT-OSS Model (14 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 14 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	~10.2B (pruned from 21B)
Original Experts per Layer	32
Pruned Experts per Layer	14
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 14 experts per layer
• No Retraining: Direct removal without additional training steps

Performance & Applications

Pruning Benefits

• Smaller Memory Footprint: 43.8% 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 14 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 14 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-10.2b-specialized-law-pruned-moe-only-14-experts", 
    torch_dtype=torch.bfloat16, 
    device_map="cpu", 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("AmanPriyanshu/gpt-oss-10.2b-specialized-law-pruned-moe-only-14-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-10.2b-specialized-law-pruned-moe-only-14-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-10.2b-specialized-law-pruned-moe-only-14-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 branches of the legal system that serve different purposes and involve different parties, procedures, and outcomes.

| Feature | Civil Law | Criminal Law |
|---|---|---|
| **Parties** | Private individuals or entities (e.g., a person suing another for damages). | The state (or government) prosecosing an individual or entity for violating a statute. |
| **Purpose** | To resolve disputes between private parties and to compensate or enforce obligations. | To maintain public order, punish wrongdoing, and deter future offenses. |
| **Burain of Proof** | “Pre‑balance of evidence” (pre‑probale) – the plaintiff must show the defendant is likely liable. | “Beyond a reasonable doubt” – the prosecution must prove the defendant’s guilt to the highest standard. |
| **Typical Cases** | Contract disputes, tort claims, property disputes, family matters (division of assets, custody). | Theft, assault, fraud, homicide, drug offenses, traffic violations. |
| **Outcome** | Monetary damages, injunctions, specific performance, or other remedies. | Penalties such as fines, imprisonment, probation, or community service. |
| **Procedure** | Usually a civil court or arbitration; parties may negotiate or settle. | Criminal court proceedings; defendant has rights to counsel, a jury (in many cases), and a presponder. |
| **Rights of the Defendant** | Right to a fair hearing, but no right to a jury in all civil cases. | Constitutional protections: right to counsel, right to a jury, protection against self‑incrimination, etc. |
| **Appeal Process** | Appeals are typically to higher courts, but the standard of review is lower. | Appeals are also possible, but the standard of review is higher and the stakes are greater. |

In short, **civil law** deals with disputes between private parties and seeks compensation or relief, while **criminal law** deals with offenses against society, seeks punishment, and is prosecuted by the state.<|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 (or “Due‑Process”) – the legal right that protects individuals from arbitrary government actions**

---

### 1.  What “Due Process” Means

* **Due Process** is a constitutional or statutory safeguard that requires the government to follow fair, transparent, and lawful procedures before depriminating a person’s life, liberty, or property.  
* It is not a single rule but a bundle of principles that apply to criminal, civil, administrative, and constitutional actions.

---

###  2.  Core Elements

| Element | What it Requires | Typical Example |
|--------|-------------------|-------------------|
| **Notice** | The person must be informed of the claim, charge, or action against them. | A summons to court, a notice of a tax audit |
| **Opportunity to Respond** | The person must have a chance to present evidence, argue, and be heard. | A hearing before a judge or a tribunal |
| **Judicial or Administrative Review** | A neutral decision-maker must evaluate the case. | A judge, a court clerk, a licensing board |
| **Right to Appeal** | The decision can be challenged in a higher forum. | Appeal to an appellate court |
| **Equal Treatment** | The same procedures apply to all similarly situated persons. | No special treatment for a particular group |
| **Transparency** | The process must be open and documented. | Public court records, written rulings |
| **Reasonable Time** | The process should not be unduly delayed. | Statistical limits on pre‑trial detention |

---

###  3.  Why It Matters

| Purpose | Impact |
|--------|--------|
| **Prevent arbitrary power** | Stops the state from acting without justification. |
| **Protect fundamental rights** | Safers life, liberty, property, and free‑speech. |
| **Maintain legitimacy** | Builds public trust in the legal system. |
| **Ensure fairness** | Guarantees that decisions are based on evidence, not bias. |

---

###  4.  How It Works in Practice

1. **Initiation**  
   *A government agency or prosecutor files a complaint or summons.*  
   *The defendant receives a notice that they are being sued or charged.*

2. **Response**  
   *The defendant files an answer, motion, or plea.*  
   *They may submit evidence, witnesses, or legal arguments.*

3.

---

Example 3

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

Response:

<|channel|>analysis<|message|>Need to answer: freedom of speech, press, religion, assembly, petition, etc.<|end|><|start|>assistant<|channel|>final<|message|>The First Amendment to the U.S. Constitution protects several core civil liberties that are considered the foundation of a free, democratic society.  In plain terms, it guarantees:

| **Right** | **What it means** |
|---|---|
| **Freedom of Speech** | Individuals may express opinions, ideas, and viewpoints without fear of government censorship or punishment. |
| **Freedom of the Press** | Newspapers, radio, television, and other media can publish news, commentary, and criticism of the government or public officials. |
| **Freedom of Religion** | People may practice any religion, or none at all, and the government may not establish a state religion or favor one over another. |
| **Freedom of Assembly** | Citizens can gather peacefully—whether in public or private spaces—to protest, celebrate, or discuss issues. |
| **Freedom to Petition the Government** | Individuals and groups can formally request the government to take action or change policy. |
| **Protection from Unreasonable Searches and Sequeshold** (often paired with the Fourth Amendment) | The amendment also bars the government from seeing or sequesting property without a warrant or probable cause. |

These rights are not absolute; courts have carved out narrow exceptions (e.g., inc‑incend speech, def‑def, or national security concerns).  Nonetheless, the First Amendment is the cornerstone of American free‑speech, press, and religious liberty.<|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