Instructions to use ScottzillaSystems/ChatGPT-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ScottzillaSystems/ChatGPT-5 with Transformers:

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

pipe = pipeline("text-generation", model="ScottzillaSystems/ChatGPT-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ScottzillaSystems/ChatGPT-5")
model = AutoModelForCausalLM.from_pretrained("ScottzillaSystems/ChatGPT-5")
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 ScottzillaSystems/ChatGPT-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ScottzillaSystems/ChatGPT-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ScottzillaSystems/ChatGPT-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ScottzillaSystems/ChatGPT-5

SGLang

How to use ScottzillaSystems/ChatGPT-5 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 "ScottzillaSystems/ChatGPT-5" \
    --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": "ScottzillaSystems/ChatGPT-5",
		"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 "ScottzillaSystems/ChatGPT-5" \
        --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": "ScottzillaSystems/ChatGPT-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ScottzillaSystems/ChatGPT-5 with Docker Model Runner:
```
docker model run hf.co/ScottzillaSystems/ChatGPT-5
```

ScottzillaSystems commited on 19 days ago

Commit

9dcd6cc

verified ·

1 Parent(s): dd667ca

fix: add pipeline_tag, library_name, and conversational tag for inference compatibility

Browse files

Files changed (1) hide show

README.md +40 -3

README.md CHANGED Viewed

@@ -1,3 +1,40 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- conversational
+- safetensors
+- qwen2
+---
+# ChatGPT-5
+Ultra-fast AI chat model based on Qwen2.5-0.5B-Instruct architecture (494M parameters).
+## Features
+- ⚡ **Ultra-fast** — Lightweight 494M parameter model for instant responses
+- 💬 **Conversational** — Optimized for multi-turn chat
+- 🔧 **Instruction Following** — Follows instructions accurately
+## Chat UI
+Try it now: [ChatGPT-5 Chat](https://huggingface.co/spaces/ScottzillaSystems/ChatGPT-5-Chat)
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("ScottzillaSystems/ChatGPT-5")
+tokenizer = AutoTokenizer.from_pretrained("ScottzillaSystems/ChatGPT-5")
+messages = [{"role": "user", "content": "Hello!"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```