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

Atomic Chat new
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

gemma-3-1b-it-reasoning

This model is a fine-tuned version of google/gemma-3-1b-it optimized for step-by-step reasoning tasks using the 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:

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'
)

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Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for the81coder/gemma-3-1b-it-reasoning

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Quantized

(456)

this model