Acrux-500M-o1-Journey

Acrux-500M-o1-Journey Model Files

The Acrux-500M-o1-Journey is a lightweight, instruction-tuned language model fine-tuned from the Qwen2.5-0.5B-Instruct base model. With a size of 500 million parameters, it is designed for cost-effective deployment and fast text generation while maintaining quality performance for instruction-following tasks.

Key Features:

1. Compact Size with Efficient Performance:
   The smaller parameter count (500M) ensures faster inference and reduced hardware requirements.

2. Instruction Optimization:
   Fine-tuned to follow prompts effectively, making it suitable for interactive applications and prompt-based tasks.

3. Domain-Specific Training:
   Trained on the GAIR/o1-journey dataset, providing tailored capabilities for specific use cases.

Training Details:

• Base Model: Qwen2.5-0.5B-Instruct
• Dataset Used for Fine-Tuning: GAIR/o1-journey
  • A compact dataset focusing on instruction-driven generation with 1.42k samples.

Capabilities:

1. Instruction Following:

   • Generates accurate and coherent responses to user instructions.
   • Handles summarization, question-answering, and conversational tasks.

2. Fast Inference:

   • Ideal for real-time applications due to reduced latency from its smaller size.

3. Interactive AI Development:

   • Suitable for chatbots, virtual assistants, and instructional interfaces.

Usage Instructions:

1. Setup:
   Download all model files, ensuring compatibility with the Hugging Face Transformers library.

2. Loading the Model:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "prithivMLmods/Acrux-500M-o1-Journey"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)
   

3. Sample Generate Text:

   input_text = "Explain the concept of machine learning in simple terms."
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs, max_length=100, temperature=0.7)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Optimize Generation:
   Adjust parameters in generation_config.json for better control of output, such as:

   • temperature for randomness.
   • top_p for sampling diversity.
   • max_length for output size.