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

Libraries

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

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

pipe = pipeline("text-generation", model="WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m")
model = AutoModelForCausalLM.from_pretrained("WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m

SGLang

How to use WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m 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 "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m" \
    --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": "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m",
		"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 "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m" \
        --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": "WithinUsAI/Gemma3-Prompt.Coder.it.Uncensored-270m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Gemma3-Prompt.Coder.it.Uncensored-270m / README.md

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	---
	base_model:
	- google/gemma-3-270m-it
	- 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-0106-1020-tuned-0106-1726
	library_name: transformers
	tags:
	- mergekit
	- merge
	datasets:
	- microsoft/rStar-Coder
	- gokaygokay/prompt-enhancement-75k
	- gokaygokay/prompt-enhancer-dataset
	---
	# Gemma-3-Prompt-Coder-270m-it (Uncensored)

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [SLERP](https://en.wikipedia.org/wiki/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


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