Instructions to use kth8/gemma-3-1b-it-System-Prompt-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kth8/gemma-3-1b-it-System-Prompt-Generator with Transformers:

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

pipe = pipeline("text-generation", model="kth8/gemma-3-1b-it-System-Prompt-Generator")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kth8/gemma-3-1b-it-System-Prompt-Generator", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kth8/gemma-3-1b-it-System-Prompt-Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kth8/gemma-3-1b-it-System-Prompt-Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kth8/gemma-3-1b-it-System-Prompt-Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kth8/gemma-3-1b-it-System-Prompt-Generator

SGLang

How to use kth8/gemma-3-1b-it-System-Prompt-Generator 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 "kth8/gemma-3-1b-it-System-Prompt-Generator" \
    --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": "kth8/gemma-3-1b-it-System-Prompt-Generator",
		"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 "kth8/gemma-3-1b-it-System-Prompt-Generator" \
        --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": "kth8/gemma-3-1b-it-System-Prompt-Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use kth8/gemma-3-1b-it-System-Prompt-Generator 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 kth8/gemma-3-1b-it-System-Prompt-Generator 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 kth8/gemma-3-1b-it-System-Prompt-Generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kth8/gemma-3-1b-it-System-Prompt-Generator to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kth8/gemma-3-1b-it-System-Prompt-Generator",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-1b-it-System-Prompt-Generator with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-1b-it-System-Prompt-Generator
```

kth8 commited on Mar 30

Commit

e0f8673

verified ·

1 Parent(s): de4b0d0

Update README.md

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@@ -4,7 +4,7 @@ language:
 - en
 base_model: unsloth/gemma-3-1b-it
 datasets:
-- kth8/system_prompts_SuperGPQA-25000x
 - kth8/system_prompts_Jobs-20000x
 pipeline_tag: text-generation
 library_name: transformers
@@ -18,7 +18,7 @@ tags:
 - gemma3_text
 ---
 ![logo](https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/gemma-3_2.original.png)
-A fine-tune of [unsloth/gemma-3-1b-it](https://huggingface.co/unsloth/gemma-3-1b-it) on the [kth8/system_prompts_SuperGPQA-25000x](https://huggingface.co/datasets/kth8/system_prompts_SuperGPQA-25000x) and [kth8/system_prompts_Jobs-20000x](https://huggingface.co/datasets/kth8/system_prompts_Jobs-20000x) dataset.
 ## Usage example
 **System prompt**

 - en
 base_model: unsloth/gemma-3-1b-it
 datasets:
+- kth8/system_prompts_SuperGPQA-26000x
 - kth8/system_prompts_Jobs-20000x
 pipeline_tag: text-generation
 library_name: transformers
 - gemma3_text
 ---
 ![logo](https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/gemma-3_2.original.png)
+A fine-tune of [unsloth/gemma-3-1b-it](https://huggingface.co/unsloth/gemma-3-1b-it) on the [kth8/system_prompts_SuperGPQA-26000x](https://huggingface.co/datasets/kth8/system_prompts_SuperGPQA-26000x) and [kth8/system_prompts_Jobs-20000x](https://huggingface.co/datasets/kth8/system_prompts_Jobs-20000x) dataset.
 ## Usage example
 **System prompt**