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aayanmishra-ml/
Atlas-Pro-7B-Preview-GGUF

Name: Atlas-Pro-7B-Preview-GGUF
Availability: InStock

transformers

gguf

text-generation-inference

unsloth

qwen2

trl

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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):

Metric	Spestly Atlas Pro (7B)	Spestly Atlas Pro (1.5B)	DeepSeek-R1-Distill-Qwen (7B)	DeepSeek-R1-Distill-Qwen (1.5B)
Average	22.65%	12.93%	11.73%	7.53%
IFEval	31.54%	24.30%	40.38%	34.63%
BBH	25.27%	9.08%	7.88%	4.73%
MATH	38.90%	25.83%	0.00%	0.00%
GPQA	11.63%	6.26%	3.91%	2.97%
MUSR	6.65%	1.86%	3.55%	2.08%
MMLU-Pro	21.89%	10.28%	14.68%	0.78%
Carbon Emissions (kg)	0.69 kg	0.59 kg	0.68 kg	0.62 kg

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:

Base Model: Built upon Atlas Flash, which is already quite capable.
Fine-Tuning Details:
- Used datasets specific to programming, math, and scientific challenges and overall reasoning abilities.
- Refined its performance for professional scenarios.
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:

python

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)

aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF