qybera2.5-0

Model Card for Qybera2.5-0

Qybera2.5-0 is a lightweight, instruction-tuned conversational AI assistant developed by Stackpulse Cloud. It is fine-tuned from the Qwen/Qwen2.5-0.5B-Instruct base model to provide helpful, accurate, and efficient responses for general-purpose chat, instruction following, and integration into the Stackpulse ecosystem.

Model Details

Model Description

Qybera2.5-0 is designed to act as a helpful assistant, optimized for low-resource environments, edge devices, and fast API routing due to its compact 0.5B parameter size. It retains the strong multilingual and reasoning capabilities of the Qwen2.5 family while being aligned to Stackpulse Cloud's helpfulness and safety guidelines.

• Developed by: Stackpulse Cloud
• Model type: Causal Language Model (Decoder-only Transformer)
• Language(s) (NLP): Multilingual (Strong proficiency in English and Chinese, with broad support for many others via the Qwen2.5 base)
• License: Apache 2.0 (Inherited from Qwen2.5)
• Finetuned from model: Qwen/Qwen2.5-0.5B-Instruct

Model Sources

• Repository: [Insert Link to your HF Repository]
• Base Model Paper: Qwen2.5 Technical Report

Uses

Direct Use

Qybera2.5-0 is intended for direct use as a conversational assistant, text summarizer, code explainer, and general instruction follower. It is highly suitable for local deployment, edge devices, or cost-effective API routing.

Downstream Use

It can be further quantized (e.g., GGUF, AWQ) for local LLM runners like Ollama or LM Studio, or integrated into larger RAG (Retrieval-Augmented Generation) pipelines as a lightweight reasoning engine.

Out-of-Scope Use

Due to its 0.5B parameter size, it is not recommended for complex, multi-step mathematical reasoning, advanced agentic workflows requiring deep logic, or generating highly creative long-form fiction without external guidance.

Bias, Risks, and Limitations

Like all LLMs, Qybera2.5-0 may occasionally hallucinate or produce biased outputs reflective of its pre-training data. Because it is a smaller model, it is more prone to factual errors on highly specialized or niche topics compared to larger (7B+) models.

Recommendations

Users should verify critical information generated by the model. It is highly recommended to use system prompts (e.g., "You are Qybera, created by Stackpulse Cloud.") to guide the model's persona and constrain its output format.

How to Get Started with the Model

Use the code below to get started with the model. Ensure you have transformers, torch, and accelerate installed.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qybera/qybera2.5-0" # Replace with your actual HF repo ID

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Qybera, created by Stackpulse Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)