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
```

gss1147 commited on Feb 7

Commit

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+---
+base_model:
+- google/gemma-3-270m-it
+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 broadfield-dev/gemma-3-270m-tuned-0106-1020 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.
+```