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--- |
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base_model: Spestly/Atlas-R1-1.5B-Preview |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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license: mit |
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language: |
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- en |
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- zh |
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- fr |
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- es |
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- pt |
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- de |
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- it |
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- ru |
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- ja |
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- ko |
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- vi |
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- th |
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- ar |
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- fa |
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- he |
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- tr |
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- cs |
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- pl |
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- hi |
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- bn |
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- ur |
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- id |
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- ms |
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- lo |
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- my |
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- ceb |
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- km |
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- tl |
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- nl |
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datasets: |
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- openai/gsm8k |
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- HuggingFaceH4/ultrachat_200k |
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library_name: transformers |
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--- |
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# **Atlas Pro** |
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### **Model Overview** |
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**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. |
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--- |
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### **Key Features** |
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- **Improved Problem-Solving:** Handles tricky tasks in programming, math, and sciences better than most models. |
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- **Advanced Code Generation:** Produces clean and efficient code, but may still miss edge cases occasionally. |
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- **Domain Expertise:** Focused on technical and scientific domains but works well in general contexts too. |
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- **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. |
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--- |
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### **Intended Use Cases** |
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Atlas Pro works best for: |
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- **Technical Professionals:** Helping developers, engineers, and scientists solve complex problems. |
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- **Educational Assistance:** Offering clear, step-by-step help for students and teachers. |
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- **Research Support:** Assisting in theoretical and applied science work. |
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- **Enterprise Tools:** Integrating into company workflows for smarter systems. |
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--- |
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### **NOTICE** |
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Atlas Pro is built on **Atlas Flash** and improved to meet high standards. Here’s how it’s made: |
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1. **Base Model:** Built upon **Atlas Flash**, which is already quite capable. |
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2. **Fine-Tuning Details:** |
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- Used datasets specific to programming, math, and scientific challenges and overall reasoning abilities. |
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- Refined its performance for professional scenarios. |
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3. **Performance Highlights:** |
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- Beats benchmarks with high accuracy, though occasional tweaks might still improve outputs. |
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--- |
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### **Limitations** |
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- **Knowledge Cutoff:** It doesn’t know about anything recent unless updated. |
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- **Hardware Requirements:** Needs high-end GPUs to run smoothly. |
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- **Specialization Bias:** While amazing in its focus areas, general chat capabilities might not be as good as other models. |
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- **Token Leakage:** In some very rare cases (~1/167), Atlas Pro will experience some token leakage. |
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--- |
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### **Licensing** |
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Atlas Pro is released under the **MIT**, which prohibits harmful uses. Make sure to follow the rules in the license agreement. |
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--- |
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### **Acknowledgments** |
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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. |
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--- |
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### **Usage** |
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You can use Atlas Pro with this code snippet: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the Atlas Pro model |
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model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Generate a response |
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prompt = "Write a Python function to calculate the Fibonacci sequence." |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
