---
base_model: Qwen/Qwen2.5-7B-Instruct-1M
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
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
library_name: transformers
datasets:
- prithivMLmods/PyCodeZone
- bespokelabs/Bespoke-Stratos-17k
- openai/gsm8k
- rubenroy/GammaCorpus-v1-50k-UNFILTERED
extra_gated_prompt: "By accessing this model, you agree to comply with ethical usage guidelines and accept full responsibility for its applications. You will not use this model for harmful, malicious, or illegal activities, and you understand that the model's use is subject to ongoing monitoring for misuse. This model is provided 'AS IS' and agreeing to this means that you are responsible for all the outputs generated by you"
extra_gated_fields:
  Name: text
  Organization: text
  Country: country
  Date of Birth: date_picker
  Intended Use:
    type: select
    options: 
      - Research
      - Education
      - Personal Development
      - Commercial Use
      - label: Other
        value: other
  I agree to use this model in accordance with all applicable laws and ethical guidelines: checkbox
  I agree to use this model under the MIT licence: checkbox
---
![Header](./Atlas-Pro-1M.png)

## **Spestly/Atlas-Pro-7B-Preview-1M**

### **Model Overview**  
**Atlas-Pro-7B-Preview-1M** is a fine-tuned version of the **Qwen2.5-7B-Instruct-1M** model, tailored for superior performance in general-purpose question answering and reasoning tasks. This model focuses on delivering clear, concise answers while maintaining a natural, conversational tone. By incorporating subtle grammatical imperfections, it creates a more relatable and human-like interaction style.  

**Key Features:**  
- **Enhanced Reasoning Capabilities:** Fine-tuning has improved the model's ability to handle reasoning-focused questions with better accuracy and depth.  
- **Humanized Interaction:** Subtle grammar imperfections are included intentionally to emulate a more human-like conversational experience.  
- **Improved QA Performance:** Extensive training has refined the model's ability to respond to questions accurately and contextually.  

---

### **Model Details**  
- **Base Model:** [Qwen/Qwen2.5-7B-Instruct-1M](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M)  
- **Fine-Tuned Dataset:** A carefully curated mix of instructional and conversational data, designed to improve reasoning and question-answering performance.  
- **Parameter Count:** 7 billion (7B)  
- **Architecture:** Transformer-based, leveraging the Qwen2.5 architecture for high efficiency and accuracy.
- **Context Window**: **1 Million Tokens**

---

### **Training Procedure**  
The model was fine-tuned using the following strategies:  
- **Dataset Quality:** A diverse dataset was selected (Public and Private), focusing on improving reasoning and conversational understanding.  
- **Humanization:** Data augmentation techniques were employed to add slight grammar imperfections, mimicking human language patterns.  
- **Optimization:** Training was conducted using mixed-precision techniques to ensure efficiency without compromising performance.  

---

### **Limitations**  
While the model excels in reasoning and answering questions, it:  
- May produce occasional inaccuracies if provided with ambiguous or incomplete queries.  
- Does not specialize in niche technical domains or highly specific knowledge areas outside its training data.  
- Subtle grammatical errors are intentional and may occasionally appear in unintended contexts.  

---

### **Usage**  
The model can be used for:  
- Interactive chatbots with a humanized tone.  
- General-purpose reasoning and question-answering tasks.  
- Personal assistant tools designed for natural communication.  

---

### **Example Usage**  
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Spestly/Atlas-Pro-7B-Preview-1M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example query
input_text = "Why is the sky blue?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

### **Community**  
We encourage feedback and contributions from the community. Please report any issues or suggest improvements via the [model’s Hugging Face page](https://huggingface.co/Spestly/Atlas-Pro-7B-Preview-1M).  

**License:** MIT

**Contact:** For questions or collaboration opportunities, please reach out via [Hugging Face](https://huggingface.co/Spestly).  

---