Instructions to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill with Transformers:

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

pipe = pipeline("text-generation", model="Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill")
model = AutoModelForImageTextToText.from_pretrained("Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill

SGLang

How to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill 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 "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill" \
    --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": "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
		"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 "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill" \
        --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": "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
    max_seq_length=2048,
)

Docker Model Runner
How to use Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill with Docker Model Runner:
```
docker model run hf.co/Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma-4 E4B Gemini 3.1 Pro Reasoning Distill

A fine-tuned version of Google's Gemma-4 E4B model, trained on high-quality chain-of-thought reasoning data distilled from Gemini 3.1 Pro.

Model Details

Property	Value
Base Model	google/gemma-4-E4B-it
Parameters	8B (4B active)
Training Method	LoRA (r=8, alpha=8)
Learning Rate	5e-5
Epochs	0.5
Framework	Unsloth

Training Data

Combined dataset from:

Roman1111111/gemini-3.1-pro-hard-high-reasoning
Roman1111111/gemini-3-pro-10000x-hard-high-reasoning

Total: ~13,000 high-quality reasoning examples covering math, logic, coding, and complex problem-solving.

Training Configuration (v2 - Improved)

This model uses conservative hyperparameters to prevent catastrophic forgetting:

# LoRA Configuration
r = 8
lora_alpha = 8
lora_dropout = 0.1

# Training Configuration
learning_rate = 5e-5
num_train_epochs = 0.5
per_device_train_batch_size = 2
gradient_accumulation_steps = 8
weight_decay = 0.01

Evaluation Results

Test Type	Score
Simple Math	3/3 (100%)
Logic Reasoning	1/1 (100%)
Complex Problems	6/8 (75%)
Overall	Matches base model

Key achievement: This fine-tuned model preserves the base model's capabilities while learning the reasoning style from training data.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill")

messages = [{"role": "user", "content": "Solve step by step: If 3x + 7 = 22, what is x?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

GGUF Versions

For llama.cpp and Ollama users, see: Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill-GGUF

License

This model inherits the Gemma license.

Acknowledgments

Google for the Gemma-4 base model
Roman1111111 for the reasoning datasets
Unsloth for efficient fine-tuning

Downloads last month: 624

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Ayodele01/gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

(197)

this model

Quantizations

1 model

Ayodele01
/

gemma-4-E4B-Gemini-3.1-Pro-Reasoning-Distill