Instructions to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF", dtype="auto")

llama-cpp-python

How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF",
	filename="Atlas-Pro-7B-Preview.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with Ollama:
```
ollama run hf.co/aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
```

Unsloth Studio new

How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF to start chatting

Docker Model Runner
How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with Docker Model Runner:
```
docker model run hf.co/aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M
```

Lemonade

How to use aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Atlas-Pro-7B-Preview-GGUF-Q4_K_M

List all available models

lemonade list

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:

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)

Downloads last month: 119

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for aayanmishra-ml/Atlas-Pro-7B-Preview-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Finetuned

aayanmishra-ml/Athena-R3-7B

Quantized

(4)

this model

Duplicated from mradermacher/Atlas-Pro-7B-Preview-GGUF

aayanmishra-ml
/

Atlas-Pro-7B-Preview-GGUF