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zeene-prod
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Model Details
Model Provider
zeene-prod
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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.
python
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"))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:
json
{ "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.
Apache-2.0. Model weights © Alibaba Cloud / Qwen team (Qwen2.5-0.5B-Instruct); redistributed unchanged. See LICENSE.
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"))