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prithivMLmods/Acrux-500M-o1-Journey

sourceHugging Facecreativeml-openrail-mupdated 2y agoView on Hugging Face

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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.

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

1.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:
2.Generates accurate and coherent responses to user instructions.
3.Handles summarization, question-answering, and conversational tasks.

1.Fast Inference:
2.Ideal for real-time applications due to reduced latency from its smaller size.

1.Interactive AI Development:
2.Suitable for chatbots, virtual assistants, and instructional interfaces.

Usage Instructions:

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

1.Loading the Model:

python

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

1.Sample Generate Text:

python

   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))

1.Optimize Generation: Adjust parameters in generation_config.json for better control of output, such as:
2.temperature for randomness.
3.top_p for sampling diversity.
4.max_length for output size. ---