README.md

---
base_model: Spestly/Atlas-Pro-1.5B-Preview
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-repo
license: mit
language:
- en
- zh
- fr
- es
- pt
- de
- it
- ru
- ja
- ko
- vi
- th
- ar
- fa
- he
- tr
- cs
- pl
- hi
- bn
- ur
- id
- ms
- lo
- my
- ceb
- km
- tl
- nl
datasets:
- openai/gsm8k
- HuggingFaceH4/ultrachat_200k
library_name: transformers
---

# Triangle104/Atlas-Pro-1.5B-Preview-Q4_K_M-GGUF
This model was converted to GGUF format from [`Spestly/Atlas-Pro-1.5B-Preview`](https://huggingface.co/Spestly/Atlas-Pro-1.5B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Spestly/Atlas-Pro-1.5B-Preview) for more details on the model.

---
Model details:
-
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.




	
		
	

		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 Astral 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-1.5B-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)

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Atlas-Pro-1.5B-Preview-Q4_K_M-GGUF --hf-file atlas-pro-1.5b-preview-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Atlas-Pro-1.5B-Preview-Q4_K_M-GGUF --hf-file atlas-pro-1.5b-preview-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Atlas-Pro-1.5B-Preview-Q4_K_M-GGUF --hf-file atlas-pro-1.5b-preview-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Atlas-Pro-1.5B-Preview-Q4_K_M-GGUF --hf-file atlas-pro-1.5b-preview-q4_k_m.gguf -c 2048
```