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

gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF / README.md

chimbiwide

Update README.md

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	---
	license: gemma
	library_name: transformers
	pipeline_tag: text-generation
	extra_gated_heading: Access Gemma on Hugging Face
	extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
	agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
	Face and click below. Requests are processed immediately.
	extra_gated_button_content: Acknowledge license
	base_model: chimbiwide/gemma-3-1b-it-thinking-32k-sft-base
	tags:
	- llama-cpp
	- gguf-my-repo
	---

	# 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](https://huggingface.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base)*
	- *[SFT Base Model Q8 GGUF](https://huggingface.co/chimbiwide/gemma-3-1b-it-thinking-32k-sft-base-Q8_0-GGUF)* <-- You're here
	- *[GRPO Full Model](https://huggingface.co/chimbiwide/gemma-3-1b-it-thinking-32k-grpo-merged)*
	- *[Q8-GGUF](https://huggingface.co/chimbiwide/gemma-3-1b-it-thinking-32k-grpo-merged-Q8_0-GGUF)*
	- *[Article](https://huggingface.co/blog/chimbiwide/gemma3think)*