Instructions to use Nanbeige/Nanbeige4.1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanbeige/Nanbeige4.1-3B with Transformers:

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

pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.1-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanbeige/Nanbeige4.1-3B")
model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.1-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nanbeige/Nanbeige4.1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanbeige/Nanbeige4.1-3B"
# 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/Nanbeige4.1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanbeige/Nanbeige4.1-3B

SGLang

How to use Nanbeige/Nanbeige4.1-3B 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/Nanbeige4.1-3B" \
    --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/Nanbeige4.1-3B",
		"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/Nanbeige4.1-3B" \
        --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/Nanbeige4.1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanbeige/Nanbeige4.1-3B with Docker Model Runner:
```
docker model run hf.co/Nanbeige/Nanbeige4.1-3B
```

Feature Request: TFLite Q4/Q6/Q8 Quantizations for Nanbeige4.1-3B

#42

by Narutoouz - opened Mar 16

Discussion

Narutoouz

Mar 16

Motivation

Enable local inference on Android phones (Snapdragon 865+, 8GB RAM) via Google AI Edge Gallery / LiteRT runtime. This 3B SLM outperforms Qwen3-32B on benchmarks but lacks mobile-optimized formats.

Current Formats Available

✅ BF16 base model
✅ GGUF quants (Q4_K_M, Q6_K, Q8_0 via mradermacher repo) [web:33]
❌ No TFLite/LiteRT conversions

Requested Formats

TFLite INT4 (Q4 equivalent) - ~1.5GB, target 4-6 tok/s on Adreno GPU
TFLite INT6 (Q6 equivalent) - ~2.5GB, balanced quality/speed
TFLite INT8 (Q8 equivalent) - ~3GB, highest fidelity

Conversion Path (for maintainers)

python

Using LiteRT Torch Generative API (recommended)

import ai_edge_torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.1-3B")

Re-author + convert + quantize per Google docs

edge_model = ai_edge_torch.convert(model, backend="odml_torch")
edge_model.export("nanbeige4.1-3b-q4.tflite")

Target Deployments

Google AI Edge Gallery (Play Store/GitHub)
LiteRT-LM runtime w/ NNAPI/GPU delegates
MediaPipe LLM Inference API

Benefits

Democratizes access to top 3B SLM on midrange phones (Mi 10T Pro with 8gb ram, etc.)
Complements GGUF ecosystem with mobile-native format
Precedents: Gemma 2B, Phi-2 have official TFLite quants

Narutoouz

Mar 16

support for Nanbeige 3b in LiteRT (before TFlite) is made here https://github.com/google-ai-edge/LiteRT/issues/6419

please create TFlite Versions of this model
check this link : https://github.com/google-ai-edge/LiteRT/issues/6419
feel free to contribute - support this model in LiteRT inference engine

Thankyou

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