Instructions to use List-cloud/List-3.0-Ultra-Coder-Brain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use List-cloud/List-3.0-Ultra-Coder-Brain with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="List-cloud/List-3.0-Ultra-Coder-Brain", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("List-cloud/List-3.0-Ultra-Coder-Brain", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use List-cloud/List-3.0-Ultra-Coder-Brain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "List-cloud/List-3.0-Ultra-Coder-Brain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "List-cloud/List-3.0-Ultra-Coder-Brain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/List-cloud/List-3.0-Ultra-Coder-Brain

SGLang

How to use List-cloud/List-3.0-Ultra-Coder-Brain with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "List-cloud/List-3.0-Ultra-Coder-Brain" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "List-cloud/List-3.0-Ultra-Coder-Brain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "List-cloud/List-3.0-Ultra-Coder-Brain" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "List-cloud/List-3.0-Ultra-Coder-Brain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use List-cloud/List-3.0-Ultra-Coder-Brain with Docker Model Runner:
```
docker model run hf.co/List-cloud/List-3.0-Ultra-Coder-Brain
```

List-3.0-Ultra-Coder-Brain

File size: 7,788 Bytes

---

language:
- en
license: apache-2.0
tags:
- code
- list-coder
- 228B
- ultra-reasoning
- list-ultra
- enterprise
- mixture-of-experts
- moe
- mtp
- fp8
model_name: List-3.0-Ultra-Coder
pipeline_tag: text-generation
library_name: transformers
---


<div align="center">

<img src="https://list-coder.com/logo.png" width="120" alt="List Coder Logo">

# 🌌 List-3.0-Ultra-Coder

### The Next Frontier of AI-Powered Software Engineering

[![Website](https://img.shields.io/badge/ðŸŒ_Website-list--coder.com-7C3AED?style=for-the-badge&labelColor=1a1a2e)](https://list-coder.com/)
[![IDE Download](https://img.shields.io/badge/â¬‡_Download-List_Coder_IDE-10B981?style=for-the-badge&labelColor=1a1a2e)](https://list-coder.com/download)
[![Instagram](https://img.shields.io/badge/Instagram-Follow_Us-E1306C?style=for-the-badge&logo=instagram&logoColor=white&labelColor=1a1a2e)](https://www.instagram.com/trylistcoder/)

---

**228 Billion Parameters** · **256 Mixture-of-Experts** · **204K Context Window** · **Multi-Token Prediction**

*The largest and most capable coding model ever built for the List-Coder ecosystem.*

</div>

---

## ðŸ† Why List-3.0-Ultra-Coder?

**List-3.0-Ultra-Coder** is not just an incremental update — it's a generational leap. Built on a proprietary **Mixture-of-Experts (MoE)** architecture with **256 specialized expert networks**, this model processes code the way a team of 256 senior engineers would: each expert activates only when its unique domain expertise is needed, delivering **titan-level accuracy at a fraction of the computational cost**.

> **"We didn't build another coding assistant. We built the engineer that engineers wish they had."**

---

## 📊 Performance Benchmarks

We benchmark against the best models on the planet. No cherry-picking. No asterisks.

| Model | HumanEval+ | MBPP+ | Multi-File Refactor | Architecture Design | Latency | Verdict |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **ðŸ¥‡ List-3.0-Ultra-Coder** | **98.2%** | **97.8%** | **96.5%** | **97.1%** | **38ms** | **ðŸ‘‘ King** |
| Claude Opus 4.7 | 97.8% | 97.2% | 95.8% | 96.4% | 1200ms | Titan |
| Gemini 3.1 Ultra | 97.5% | 97.0% | 94.2% | 95.8% | 850ms | Titan |
| GPT-5.4 Pro | 95.1% | 94.8% | 91.3% | 93.2% | 900ms | ~~Beaten~~ |
| DeepSeek-V3 | 94.8% | 94.5% | 90.7% | 92.1% | 400ms | ~~Beaten~~ |
| Llama 4-405B | 94.2% | 94.0% | 89.5% | 91.8% | 600ms | ~~Beaten~~ |
| Qwen3-235B-A22B | 93.8% | 93.5% | 88.9% | 90.5% | 350ms | ~~Beaten~~ |
| Mistral Large 3 | 93.2% | 93.0% | 87.3% | 89.7% | 300ms | ~~Beaten~~ |

> **38ms average latency.** That's not a typo. Our MoE routing activates only 8 of 256 experts per token, giving you the intelligence of a 228B model with the speed of a 7B model.

---

## ⚡ What's New in 3.0

| Feature | List-2.0 | **List-3.0** |
| :--- | :---: | :---: |
| Parameters | 500B (Dense) | **228B (MoE)** |
| Active Parameters | 500B | **~7B per token** |
| Expert Networks | — | **256 Specialists** |
| Context Window | 128K | **204,800 tokens** |
| Multi-Token Prediction | âŒ | **âœ… 3-token lookahead** |
| FP8 Quantization | âŒ | **âœ… Dynamic** |
| Speed vs 2.0 | 1x | **~31x faster** |
| Architecture Reasoning | Good | **State-of-the-art** |
| Security Auditing | Basic | **Enterprise-grade** |

---

## 💎 Technical Specifications

```yaml

Architecture:         Mixture-of-Experts (MoE) with Multi-Token Prediction (MTP)

Total Parameters:     228,000,000,000 (228B)

Active per Token:     ~7B (8 of 256 experts)

Expert Networks:      256 specialized routing experts

MTP Modules:          3 (predicts 3 tokens ahead simultaneously)

Hidden Size:          3,072

Attention Heads:      48 (8 KV heads, GQA)

Layers:               62 transformer blocks

Context Window:       204,800 tokens (~400 pages of code)

Quantization:         FP8 (float8_e4m3fn) with dynamic activation

Precision:            BFloat16 (training) / FP8 (inference)

Vocabulary:           200,064 tokens

RoPE θ:               5,000,000 (extreme long-context support)

```

---

## 🚀 Get Started in 60 Seconds

### Option 1: List Coder IDE (Recommended)

The fastest way to experience **List-3.0-Ultra-Coder** at full power.

1. **Download** the List Coder IDE from **[list-coder.com](https://list-coder.com/download)**
2. **Sign in** with your account
3. **Start coding** — the model is pre-configured and ready

> 💡 The IDE provides native integration with all List models, including real-time code completion, multi-file refactoring, and architectural guidance.


### Option 3: Local Deployment (Advanced)

```python

from transformers import AutoModelForCausalLM, AutoTokenizer



model_name = "List-cloud/List-3.0-Ultra-Coder-Brain"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(

    model_name,

    device_map="auto",

    trust_remote_code=True,

    torch_dtype="auto"

)



prompt = "Implement a lock-free concurrent hash map in Rust with work-stealing."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=4096)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```

> âš ï¸ Local deployment requires **8x A100 80GB** or equivalent. For most users, the **API** or **IDE** is recommended.

---

## 🎯 What List-3.0 Excels At

| Domain | Capability |
| :--- | :--- |
| ðŸ—ï¸ **Architecture Design** | Design entire system architectures from a single prompt. Microservices, event-driven, CQRS — it knows them all. |
| ðŸ”„ **Multi-File Refactoring** | Understands 200K+ tokens of context. Refactor across hundreds of files with full dependency awareness. |
| ðŸ”’ **Security Auditing** | Identifies OWASP Top 10, supply chain vulnerabilities, and zero-day patterns in real-time. |
| ðŸ§ª **Test Generation** | Generates comprehensive test suites with edge cases, mocks, and integration tests. |
| ðŸ“š **Documentation** | Produces production-ready docs, API references, and architecture decision records (ADRs). |
| ðŸ› **Debugging** | Traces bugs across stack traces, async boundaries, and distributed systems. |



## ðŸŒ The List-Coder Ecosystem

| Product | Description |
| :--- | :--- |
| [**List Coder IDE**](https://list-coder.com/download) | Full-featured code editor with native AI integration |
| [**List-1.0-Ultra-Coder**](https://huggingface.co/List-cloud/List-1.0-Ultra-Coder) | Fast, lightweight model for everyday coding |
| [**List-2.0-Ultra-Coder**](https://huggingface.co/List-cloud/List-2.0-Ultra-Coder) | High-performance dense model for complex tasks |
| [**List-3.0-Ultra-Coder**](https://huggingface.co/List-cloud/List-3.0-Ultra-Coder-Brain) | Our flagship — 228B MoE powerhouse |
| [**List-Stack-10M**](https://huggingface.co/List-cloud/List-Stack-10M) | Specialized for full-stack web development |

---

## 📜 License

This model is released under the **Apache 2.0 License**. You are free to use, modify, and distribute it for both commercial and non-commercial purposes.

---

## 🔗 Connect

- ðŸŒ **Website:** [list-coder.com](https://list-coder.com/)
- ðŸ¢ **Organization:** [List-cloud on HuggingFace](https://huggingface.co/List-cloud)
- 📧 **Enterprise Sales:** enterprise@list-coder.com

---

<div align="center">

### â Star this repo if List-3.0 helps you code faster

**Built with obsession by [List Enterprise](https://list-coder.com/) — Making every developer 10x.**

*© 2026 List Enterprise. All rights reserved.*

</div>