Instructions to use pool-water/script-kiddie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pool-water/script-kiddie with Transformers:

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

pipe = pipeline("text-generation", model="pool-water/script-kiddie")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pool-water/script-kiddie")
model = AutoModelForCausalLM.from_pretrained("pool-water/script-kiddie")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pool-water/script-kiddie with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pool-water/script-kiddie"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pool-water/script-kiddie",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pool-water/script-kiddie

SGLang

How to use pool-water/script-kiddie 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 "pool-water/script-kiddie" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pool-water/script-kiddie",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "pool-water/script-kiddie" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pool-water/script-kiddie",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pool-water/script-kiddie with Docker Model Runner:
```
docker model run hf.co/pool-water/script-kiddie
```

WHATEVER420 commited on Aug 29, 2025

Commit

0ce977e

verified ·

1 Parent(s): dc1de93

:pencil: update model card

Browse files

![script-kiddy.png](https://cdn-uploads.huggingface.co/production/uploads/63f2955bf4e30ffd2bd607ae/7khK7ajTppA0yWcgntk5l.png)

Files changed (1) hide show

README.md +19 -32

README.md CHANGED Viewed

@@ -1,8 +1,18 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
@@ -35,7 +45,8 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
@@ -140,17 +151,11 @@ Use the code below to get started with the model.
 ## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
 ### Model Architecture and Objective
@@ -158,7 +163,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ### Compute Infrastructure
-[More Information Needed]
 #### Hardware
@@ -178,22 +183,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 **APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
 [More Information Needed]

 ---
 library_name: transformers
+license: mit
+datasets:
+- WHATEVER420/script-kiddy-instruction-manual
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
 ---
+![script-kiddy.png](https://cdn-uploads.huggingface.co/production/uploads/63f2955bf4e30ffd2bd607ae/7khK7ajTppA0yWcgntk5l.png)
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ## Uses
+This software is provided strictly for educational and research purposes only. It is intended to help users learn, experiment, and study relevant concepts. The authors and contributors do not endorse or condone any misuse of this software. Use of this software for malicious, unlawful, or unauthorized activities is strictly prohibited, and users assume full responsibility for compliance with all applicable laws and regulations.
 ### Direct Use
 ## Environmental Impact
+- **Hardware Type:** A100
+- **Hours used:** 0.75 hours
+- **Cloud Provider:** [RunPod
+- **Compute Region:** KS-2
+- **Carbon Emitted:** ~0.08 kg
 ### Model Architecture and Objective
 ### Compute Infrastructure
+- Trained for 45 minutes on a single A100
 #### Hardware
 **APA:**
 [More Information Needed]