Instructions to use the81coder/gemma-3-1b-it-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use the81coder/gemma-3-1b-it-reasoning with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
# Run inference directly in the terminal:
llama cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
# Run inference directly in the terminal:
llama cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf the81coder/gemma-3-1b-it-reasoning:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf the81coder/gemma-3-1b-it-reasoning:Q8_0

Use Docker

docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0

LM Studio
Jan

vLLM

How to use the81coder/gemma-3-1b-it-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "the81coder/gemma-3-1b-it-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "the81coder/gemma-3-1b-it-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0

Ollama
How to use the81coder/gemma-3-1b-it-reasoning with Ollama:
```
ollama run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
```

Unsloth Studio

How to use the81coder/gemma-3-1b-it-reasoning 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 the81coder/gemma-3-1b-it-reasoning 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 the81coder/gemma-3-1b-it-reasoning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for the81coder/gemma-3-1b-it-reasoning to start chatting

Docker Model Runner
How to use the81coder/gemma-3-1b-it-reasoning with Docker Model Runner:
```
docker model run hf.co/the81coder/gemma-3-1b-it-reasoning:Q8_0
```

Lemonade

How to use the81coder/gemma-3-1b-it-reasoning with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull the81coder/gemma-3-1b-it-reasoning:Q8_0

Run and chat with the model

lemonade run user.gemma-3-1b-it-reasoning-Q8_0

List all available models

lemonade list

Atomic Chat

gemma-3-1b-it-reasoning

File size: 1,238 Bytes

---
base_model: google/gemma-3-1b-it
tags:
- gemma-3
- text-generation
- reasoning
---

# gemma-3-1b-it-reasoning

This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) optimized for step-by-step reasoning tasks using the [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) dataset.

### Model Description
- **Developed by:** the81coder
- **Model type:** Gemma 3
- **Language(s):** English
- **License:** Gemma Terms of Use
- **Fine-tuned from model:** google/gemma-3-1b-it

### Training Procedure
The model was fine-tuned using QLoRA with the following configurations:
- **Learning Rate:** 1e-5
- **Batch Size:** 1 (with 4 accumulation steps)
- **Optimizer:** AdamW
- **Precision:** bfloat16
- **Target Modules:** q_proj, v_proj, k_proj, o_proj


### Usage
You can use this model with the `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = 'the81coder/gemma-3-1b-it-reasoning'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map='auto'
)
```