zubr-tiny-2b

Model Overview

zubr-tiny-2b is a lightweight, fine-tuned conversational language model based on the state-of-the-art Qwen 3.5 (and Qwen 2.5 architecture ecosystem) developed by alekringtonnn-ai.

By leveraging the powerful foundations of the Qwen series, this 2-billion parameter model offers exceptional multi-lingual capabilities, reasoning, and instruction-following proficiency while maintaining an incredibly small hardware footprint. It is highly optimized for fast local inference, low RAM/VRAM consumption, and efficient deployment on consumer-grade hardware such as laptops and edge devices.

How to Download via Terminal

You can easily download the model weights and configuration files directly from Hugging Face using your terminal. Choose one of the methods below:

Method 1: Using Hugging Face CLI (Recommended)

This is the most efficient method to download the repository or specific model shards.

1. Install or update the Hugging Face Hub CLI:

   pip install -U huggingface_hub
   

2. Download the complete repository:

   huggingface-cli download alekringtonnn-ai/zubr-tiny-2b
   

3. Download to a specific local directory:

   huggingface-cli download alekringtonnn-ai/zubr-tiny-2b --local-dir ./zubr-tiny-2b
   

Method 2: Using Git LFS

If you prefer working with standard Git workflows, make sure Git Large File Storage is installed.

1. Initialize Git LFS:

   git lfs install
   

2. Clone the repository:

   git clone https://huggingface.co
   

Method 3: Direct Download via cURL

To pull specific configurations or single files without Python dependencies:

curl -L -O https://huggingface.co/resolve/main/config.json

Quick Start (Python)

Since the model is based on Qwen, it is fully compatible with the standard transformers library. You can run it locally using the following snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "alekringtonnn-ai/zubr-tiny-2b"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    device_map="auto", 
    torch_dtype="auto"
)

# Format your prompt
prompt = "Привет! Расскажи о себе."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate response
inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
]

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

Features & Limitations

• Base Architecture: Qwen 3.5 / Qwen 2.5 2B.
• Context Length: Inherits the extended context window support from the base Qwen architecture.
• Target Use Case: Perfect for private local chatbots, text summarization, and embedded tasks where resources are highly constrained.