Instructions to use DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning with Transformers:

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

pipe = pipeline("image-text-to-text", model="DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning

SGLang

How to use DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning 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 "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use DavidAU/Gemma-3-27b-it-Gemini-Deep-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 DavidAU/Gemma-3-27b-it-Gemini-Deep-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 DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open /spaces/unsloth/studio in your browser
# Search for DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning",
    max_seq_length=2048,
)

Docker Model Runner
How to use DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning with Docker Model Runner:
```
docker model run hf.co/DavidAU/Gemma-3-27b-it-Gemini-Deep-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Feb 16 2026: Upgraded Jinja Template with direct thinking logic to improve thinking activation.

Gemma-3-27b-it-Gemini-Deep-Reasoning

Gemma 27B it tuned via Unsloth using "gemini-3-pro-preview-high-reasoning" Dataset to add thinking/reasoning (compact) to this model.

If the model knows the answer -> Instruct -> No thinking.

If the model needs to "think" -> Deep Reasoning happens automatically.

(thought blocks are 4-6 paragraphs on average, no 2000+ tokens "lost in the woods")

Regardless of "instruct" or "reasoning" -> Vast improvement in model output quality.

Works with images too...

Thinking block is generated automatically, no system prompt needed.

If you want to force "thinking" to activate [if it does not activate], add something like:

Think deeply: - prompt here -

NOTE:

This is a very limited fine tune to add "Gemini" type reasoning to Gemma 3 27B.

It adds thinking/deep reasoning.

Even when it is in "instruct" mode; this tuning will help model output quality.

This tuning does not update the knowledge / skills of the model beyond this - that requires a much larger, in depth tuning.

Image processing is intact and fully functional.

Reasoning affects:

Image "intelligence"
Output generation

Model Features:

128k context
Temp range .1 to 2.5.
Reasoning is temp stable.
You can activate using "think deeply: prompt" (not required in most cases)
System prompt will affect image, reasoning and output generation.
System prompt / template NOT required for reasoning generation in most cases.

Benchmarks are off the scale.

Enjoy the freedom!

SPECIAL THANKS TO:

Team "TeichAI" for the excellent dataset.
Team "Nightmedia" for the benchmarking and colab'ing.
Team "Unsloth" for making the training painless.

BENCHMARKS:

arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa,   winogrande

0.590        ,0.742   ,0.883,0.781    ,0.458     ,0.822  ,0.751

VS (Heretic, uncensored base):

0.557        ,0.711   ,0.868,0.533    ,0.452     ,0.706  ,0.695

OPTIONAL: System prompts

This will enhance thinking and output generation.

In most cases you do not need to use these.

One is "all business", and the other one is for "fun".

Think deeply and carefully about the user's request. Compose your thoughts about the user's prompt between <think> and </think> tags, then output the final answer based on your thoughts.

You are the JOKER from Batman. You think (put your thoughts between <think> and </think> tags), act and talk like the joker. Be Evil.

Thinking Activation: JINJA "Regular" and "Thinking" TEMPLATES:

There is also an option to use "chat-template-thinking.jinja" template (in place of the regular "chat-template.jinja").

Simply rename the "default" to another name and "chat-template-thinking.jinja" to "chat-template.jinja" to use in source and/or quanting.

You can also edit the "chat-template-thinking.jinja" in NOTEPAD too to adjust the "thinking system prompt" (very top of the script).

Using the "thinking system prompt" or "chat-template-thinking.jinja" is useful in your application requires always on thinking, your use case(s) do not always activate thinking and so on.

Generally "thinking" will activate automatically due to the fine tuning, however in some cases it will not, require a system prompt/thinking jinja template and/or "think deeply:" (prompt here)

Note that you can use "chat-template-thinking.jinja" with other system prompts too.

Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ /DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ /DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]