Instructions to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF 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 chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF: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 chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF: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 chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0

SGLang

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF 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 "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF" \
    --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": "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF",
		"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 "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF" \
        --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": "chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with Ollama:
```
ollama run hf.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF 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 chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF 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 chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0
```

Lemonade

How to use chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF-Q8_0

List all available models

lemonade list

GemmaThink-32k (SFT Base Model)

This model was trained using SFT (Suprevised FineTuning) to generate structured reasoning traces.

Training Details

Base Model: google/gemma-3-1b-it
Training Method: SFT + GRPO
LoRA Rank: 32
LoRA Alpha: 64.0
Framework: Tunix (JAX)
Hardware: v6e-1 TPU in Colab

Output Format

<reasoning>step-by-step thinking process</reasoning>
<answer>final answer</answer>

Quicklinks:

SFT Base Model
SFT Base Model Q8 GGUF <-- You're here
GRPO Full Model
Q8-GGUF
Article

Downloads last month: 8

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

8-bit

Model tree for chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF

Base model

chimbiwide/gemma-3-1b-it-thinking-32k-sft-base

Quantized

(1)

this model

Collection including chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF

GemmaThink

Collection

A collection of Gemma3-1b-it models that we post-trained using SFT and GRPO to enhance its reasoning capabilities, using Google's new Tunix library. • 7 items • Updated Feb 27