Instructions to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF", dtype="auto")

llama-cpp-python

How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF",
	filename="Gemma-3-Prompt-Coder-270m-it-Uncensored.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

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 WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

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 WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
```

Unsloth Studio new

How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-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 WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-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 WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF to start chatting

Docker Model Runner
How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Gemma3-Prompt.Coder.Uncensored.270m-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma3-Prompt.Coder.Uncensored.270m-GGUF-Q4_K_M

List all available models

lemonade list

Gemma-3-Prompt-Coder-270m-it (Uncensored)

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

huihui-ai-Huihui-gemma-3-270m-it-abliterated
AxionLab-official-DogeAI-v1.5-Coder
gokaygokay-prompt-enhancer-gemma-3-270m-it
broadfield-dev-gemma-3-270m-tuned-0106-1726

This Is a fine-tuned model based on google/gemma-3-270m-it for enhancing and expanding short prompts into detailed, context-rich descriptions.
This is an uncensored version of google/gemma-3-270m-it, achieved through fine-tuning with the TRL framework.
This model is a fine-tuned version of google/gemma-3-270m-it on the microsoft/rStar-Coder dataset.

****Usage Warnings Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. ```