Instructions to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M-Instruct")
model = PeftModel.from_pretrained(base_model, "sapbot/gemma-3n-4b-it-distill-smollm2-360m")

Transformers

How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with Transformers:

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

pipe = pipeline("text-generation", model="sapbot/gemma-3n-4b-it-distill-smollm2-360m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sapbot/gemma-3n-4b-it-distill-smollm2-360m", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sapbot/gemma-3n-4b-it-distill-smollm2-360m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sapbot/gemma-3n-4b-it-distill-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sapbot/gemma-3n-4b-it-distill-smollm2-360m

SGLang

How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m 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 "sapbot/gemma-3n-4b-it-distill-smollm2-360m" \
    --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": "sapbot/gemma-3n-4b-it-distill-smollm2-360m",
		"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 "sapbot/gemma-3n-4b-it-distill-smollm2-360m" \
        --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": "sapbot/gemma-3n-4b-it-distill-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sapbot/gemma-3n-4b-it-distill-smollm2-360m with Docker Model Runner:
```
docker model run hf.co/sapbot/gemma-3n-4b-it-distill-smollm2-360m
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma 3n 4B Distill SmolLM2 360M Instruct

WARNING: REMEMBER TO ADD CUSTOM SYSTEM PROMPT RESEMBLING GEMMA 3N 4B IF YOU WANT MODEL TO KNOW THAT IT'S IT, BECAUSE THE ONLY THING CHANGED IS STYLE, IN DATASET THERE WERE NO SIGNS OF TEACHER MODEL. HAVE FUN.

This model is a fine-tuned version of unsloth/SmolLM2-360M-Instruct on the sapbot/gemma-3n-4b-it-423x dataset.

Model description

This model was distilled from Gemma 3n 4B, essentially as a demonstration of purpose of my datasets.

Intended uses & limitations

As a demonstration of my datasets

Limitations are easy: it's still SmolLM2, just with... a little bit of google taste.

Training and evaluation data

Training procedure

Used default parameters from LLaMA-Factory.

P.S. Waiting for Unsloth Studio official ROCm support, LLaMA-Factory was pain to use as inexpirienced user in that field.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 3.0
mixed_precision_training: Native AMP

Device

Trained entirely on one AMD ATI Radeon RX 6600 just for 30min. It was NOT QLoRA, but just a LoRA! What to say, really fast!

Framework versions

PEFT 0.18.1
Transformers 5.2.0
Pytorch 2.6.0+rocm6.1
Datasets 4.0.0
Tokenizers 0.22.2

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Model tree for sapbot/gemma-3n-4b-it-distill-smollm2-360m

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Finetuned

unsloth/SmolLM2-360M-Instruct

Adapter

(157)

this model

Quantizations

1 model

sapbot
/

gemma-3n-4b-it-distill-smollm2-360m