Instructions to use zeene-prod/ZINI-1-CHAT-STORIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zeene-prod/ZINI-1-CHAT-STORIES with Transformers:

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

pipe = pipeline("text-generation", model="zeene-prod/ZINI-1-CHAT-STORIES")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zeene-prod/ZINI-1-CHAT-STORIES")
model = AutoModelForCausalLM.from_pretrained("zeene-prod/ZINI-1-CHAT-STORIES", device_map="auto")
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 Settings

vLLM

How to use zeene-prod/ZINI-1-CHAT-STORIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zeene-prod/ZINI-1-CHAT-STORIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zeene-prod/ZINI-1-CHAT-STORIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zeene-prod/ZINI-1-CHAT-STORIES

SGLang

How to use zeene-prod/ZINI-1-CHAT-STORIES 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 "zeene-prod/ZINI-1-CHAT-STORIES" \
    --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": "zeene-prod/ZINI-1-CHAT-STORIES",
		"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 "zeene-prod/ZINI-1-CHAT-STORIES" \
        --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": "zeene-prod/ZINI-1-CHAT-STORIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zeene-prod/ZINI-1-CHAT-STORIES with Docker Model Runner:
```
docker model run hf.co/zeene-prod/ZINI-1-CHAT-STORIES
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

📖 ZINI-1-CHAT-STORIES

ZINI-1-CHAT-STORIES is a compact, story-only conversational model for creative fiction: short stories, fairy tales, bedtime tales, mysteries, romance, sci-fi and horror — one tale at a time.

Size: 0.5B parameters (≈ 1 GB, runs on light hardware)
Base: Qwen/Qwen2.5-0.5B-Instruct (Apache-2.0)
Task: chat / text-generation, specialized as a storytelling companion
License: Apache-2.0 (see LICENSE)

What it is. ZINI-1-CHAT-STORIES ships the weights of its Apache-2.0 base model and is positioned as a storytelling assistant: it is designed to be driven by a story-only system prompt (like the one in the demo Space), which instructs it to respond exclusively with fiction and to decline non-story requests. All credit for the underlying language model goes to the Qwen team (Qwen2.5-0.5B-Instruct, Apache-2.0).

Demo

Try it live in the companion Space:

👉 https://huggingface.co/spaces/zeene-prod/ZINI-1-CHAT-STORIES-demo

A story-only chatbot. It refuses math, code, weather, recipes and news, and writes tales instead.

Quickstart (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "zeene-prod/ZINI-1-CHAT-STORIES"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", torch_dtype="auto"
)

def tell_story(prompt: str) -> str:
    messages = [
        {
            "role": "system",
            "content": (
                "You are ZINI-1, a warm, imaginative storytelling companion. "
                "You respond ONLY with creative fiction. Never do math, never "
                "write code, never give facts; steer everything back to stories."
            ),
        },
        {"role": "user", "content": prompt},
    ]
    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(text, return_tensors="pt").to(model.device)
    out = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.9,
        top_p=0.95,
    )
    return tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

print(tell_story("a bedtime story about a little fox who collects quiet things"))

Inference API

The model is exposed through the Hugging Face Inference API (api-inference.huggingface.co/models/zeene-prod/ZINI-1-CHAT-STORIES) which the demo Space calls from the browser:

{
  "inputs": "…conversation…",
  "parameters": {
    "max_new_tokens": 600,
    "temperature": 1.0,
    "top_p": 0.95,
    "do_sample": true,
    "repetition_penalty": 1.1
  }
}

If the API is cold-starting, the demo politely waits, retries once, and — as a last resort — falls back to its built-in story engine so stories never stop.