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

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
• 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

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 .

License: MIT

Contact: For questions or collaboration opportunities, please reach out via Hugging Face .