Pluto-AI-Labs/Atlas-Frontier-Model-Traces

🧬 Atlas-Frontier-Model-Traces

A universal ChatML dataset distilling the agentic coding capabilities of frontier models (Kimi-K3, GPT-5.6-Sol, and Fable-5).

Dataset Description

Atlas-Frontier-Model-Traces is a meticulously curated dataset containing 15,746 coding and debugging traces generated by three of the most advanced frontier AI models.

This dataset is designed for Knowledge Distillation. By training smaller open-source language models (such as Qwen, Llama, Mistral, and Gemma) on the output traces of frontier-scale models, developers can transfer sophisticated coding behaviors into efficient edge-deployable models.

Data Sources

This dataset is created by combining and rigorously cleaning the following public datasets:

• —greghavens/kimi-k3-coding-and-debugging-traces
• —greghavens/gpt-5.6-sol-coding-and-debugging-traces
• —greghavens/fable-5-coding-and-debugging-traces

Dataset Structure

The dataset has been converted into a universal ChatML format compatible with virtually every modern LLM architecture.

Example Entry

<|im_start|>user
Write a Python function to connect to a PostgreSQL database.
<|im_end|>
<|im_start|>assistant
import psycopg2

def connect_to_db(dbname, user, password, host, port):
    # Function implementation here...
<|im_end|>

Data Processing & Cleaning Pipeline

To maximize quality and eliminate schema inconsistencies, the dataset was processed using a robust MLOps pipeline.

• —Pandas Bypass Loading
• —Loaded raw datasets with Pandas to avoid Hugging Face schema conflicts across repositories.

• —Aggressive Schema Unification
• —Converted ShareGPT, OpenAI Messages, Prompt/Completion, and other formats into one standardized schema.

• —Quality Filtering
• —Removed broken samples and assistant responses shorter than 15 characters.

• —Universal ChatML Formatting
• —Extracted hardcoded system prompts from conversations and stored them in a separate column.

• —Parquet Compression
• —Reduced over 1 GB of raw data into a compact 36.1 MB high-signal Parquet dataset.

How to Use

from datasets import load_dataset

# Load dataset
ds = load_dataset(
    "Siddh07ETH/Atlas-Frontier-Model-Traces",
    split="train"
)

custom_sys_prompt = "You are a helpful coding assistant."

def format_for_training(example):
    return {
        "final_text":
        f"<|im_start|>system\n"
        f"{custom_sys_prompt}"
        f"<|im_end|>\n"
        f"{example['text']}"
    }

ds = ds.map(format_for_training)

print(ds[0]["final_text"])

Intended Uses

This dataset is intended for:

• —Knowledge Distillation
• —Instruction Tuning
• —Supervised Fine-Tuning (SFT)
• —Coding Assistants
• —Agentic Tool Use
• —Debugging Models
• —Fine-tuning 0.5B–7B parameter language models

Supported architectures include:

• —Qwen
• —Llama
• —Mistral
• —Gemma
• —Other ChatML-compatible models

Limitations

• —Focused exclusively on coding and debugging tasks.
• —Does not contain general conversational or multilingual data.
• —Optimized for functional code generation and debugging rather than long-form software architecture discussions.

Citation

If you use this dataset in your research or projects, please cite both this dataset and the original source datasets where appropriate.

License

This dataset is distributed under the Apache 2.0 License, inheriting the licensing terms of the original source datasets.