Instructions to use Nanbeige/Nanbeige4.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanbeige/Nanbeige4.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.2-3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.2-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanbeige/Nanbeige4.2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanbeige/Nanbeige4.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanbeige/Nanbeige4.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanbeige/Nanbeige4.2-3B

SGLang

How to use Nanbeige/Nanbeige4.2-3B 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 "Nanbeige/Nanbeige4.2-3B" \
    --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": "Nanbeige/Nanbeige4.2-3B",
		"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 "Nanbeige/Nanbeige4.2-3B" \
        --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": "Nanbeige/Nanbeige4.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanbeige/Nanbeige4.2-3B with Docker Model Runner:
```
docker model run hf.co/Nanbeige/Nanbeige4.2-3B
```

leran1995 commited on 2 days ago

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Update README.md

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README.md +6 -1

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@@ -16,6 +16,11 @@ base_model:
 <img src="figures/nbg.png" width="220" alt="Nanbeige Logo">
 </div>
 # <span id="Introduction">1. Introduction</span>
 Nanbeige4.2-3B is a compact agentic model built on [Nanbeige4.2-3B-Base](https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base), designed to combine strong agentic behavior with broad reasoning and alignment capabilities. Its Looped Transformer architecture reuses the transformer layers to increase model capacity without adding parameters. With only 3B non-embedding parameters, the model delivers solid performance on general-agent and code-agent tasks.
@@ -35,7 +40,7 @@ Key strengths include:
 - **Local Personal Assistant**: When integrated with an agentic scaffold designed for personal workflows (e.g., OpenClaw), Nanbeige4.2-3B can support extended tasks spanning daily assistance, office work, and deep research.
-> The accompanying `modeling_nanbeige.py` also includes our latest architectural improvements, including **LoopSplit**, **mHC with depth attention**, and **concatenated n-gram embeddings**. These features have been incorporated into Nanbeige4.5, whose training is underway for release later in 2026.
 # <span id="Model-Performance">2. Model Performance</span>

 <img src="figures/nbg.png" width="220" alt="Nanbeige Logo">
 </div>
+<p align="center">
+  <a href="https://huggingface.co/Nanbeige/Nanbeige4.2-3B/blob/main/Nanbeige42_report.pdf"><b>Technical Report</b>👁️</a>
+</p>
 # <span id="Introduction">1. Introduction</span>
 Nanbeige4.2-3B is a compact agentic model built on [Nanbeige4.2-3B-Base](https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base), designed to combine strong agentic behavior with broad reasoning and alignment capabilities. Its Looped Transformer architecture reuses the transformer layers to increase model capacity without adding parameters. With only 3B non-embedding parameters, the model delivers solid performance on general-agent and code-agent tasks.
 - **Local Personal Assistant**: When integrated with an agentic scaffold designed for personal workflows (e.g., OpenClaw), Nanbeige4.2-3B can support extended tasks spanning daily assistance, office work, and deep research.
+> The accompanying [`modeling_nanbeige.py`](https://huggingface.co/Nanbeige/Nanbeige4.2-3B/blob/main/modeling_nanbeige.py) also includes our latest architectural improvements, including **LoopSplit**, **mHC with depth attention**, and **concatenated n-gram embeddings**. These features have been incorporated into Nanbeige4.5, whose training is underway for release later in 2026.
 # <span id="Model-Performance">2. Model Performance</span>