prithivMLmods/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.

File Name	Size	Description	Upload Status
.gitattributes	1.57 kB	Git attributes for managing LFS files.	Uploaded
README.md	195 Bytes	Model overview or documentation.	Updated
added_tokens.json	657 Bytes	Custom tokens for the tokenizer.	Uploaded
config.json	859 Bytes	Model configuration file.	Uploaded
generation_config.json	280 Bytes	Configuration for text generation.	Uploaded
merges.txt	1.82 MB	Merge rules for byte-pair encoding (BPE).	Uploaded
pytorch_model.bin	988 MB	Model weights (PyTorch format).	Uploaded (LFS)
special_tokens_map.json	644 Bytes	Mapping for special tokens.	Uploaded
tokenizer.json	11.4 MB	Full tokenizer configuration.	Uploaded (LFS)
tokenizer_config.json	7.73 kB	Additional tokenizer settings.	Uploaded
vocab.json	2.78 MB	Vocabulary for the tokenizer.	Uploaded

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.

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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.

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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.

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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.

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