Spestly / Atlas-Pro-7B-Preview-GGUF

Atlas Pro

Model Overview

Atlas Pro (Previously known as '🏆 Atlas-Experiment 0403 🧪' in AtlasUI) is an advanced language model (LLM) built on top of Atlas Flash . It's designed to provide exceptional performance for professional tasks like coding, mathematics, and scientific problem-solving. Atlas Pro builds on Atlas Flash by adding more fine-tuning and specialization, making it perfect for researchers and advanced users.

Key Features

• Improved Problem-Solving: Handles tricky tasks in programming, math, and sciences better than most models.
• Advanced Code Generation: Produces clean and efficient code, but may still miss edge cases occasionally.
• Domain Expertise: Focused on technical and scientific domains but works well in general contexts too.
• Reasoning Improvement: In this version of Atlas, I have enhanced it's reasoning via synthetic data from models such as Gemini-2.0 Flash Thinking so that it can improve on reasoning.

Evaluation

Below are the evaluations of the Atlas-Pro models and Deepseek's R1 Qwen Distills (The model that started the whole Atlas family):

Intended Use Cases

Atlas Pro works best for:

• Technical Professionals: Helping developers, engineers, and scientists solve complex problems.
• Educational Assistance: Offering clear, step-by-step help for students and teachers.
• Research Support: Assisting in theoretical and applied science work.
• Enterprise Tools: Integrating into company workflows for smarter systems.

NOTICE

Atlas Pro is built on Atlas Flash and improved to meet high standards. Here’s how it’s made:

1. Base Model: Built upon Atlas Flash , which is already quite capable.
2. Fine-Tuning Details:
   • Used datasets specific to programming, math, and scientific challenges and overall reasoning abilities.
   • Refined its performance for professional scenarios.
3. Performance Highlights:
   • Beats benchmarks with high accuracy, though occasional tweaks might still improve outputs.

Limitations

• Knowledge Cutoff: It doesn’t know about anything recent unless updated.
• Hardware Requirements: Needs high-end GPUs to run smoothly.
• Specialization Bias: While amazing in its focus areas, general chat capabilities might not be as good as other models.
• Token Leakage: In some very rare cases (~1/167), Atlas Pro will experience some token leakage.

Licensing

Atlas Pro is released under the MIT , which prohibits harmful uses. Make sure to follow the rules in the license agreement.

Acknowledgments

Created by Spestly as part of the Atlas Model Family , Atlas Pro builds on the strong foundation of Atlas Flash . Special thanks to Deepseek's R1 Qwen Distilles for helping make it happen.

Usage

You can use Atlas Pro with this code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-7B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)