🧬 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.

Column	Type	Description
text	string	ChatML conversation containing only user and assistant turns
system_prompt	string	Suggested system prompt stored separately for easier customization
source_model	string	Metadata indicating the originating frontier model

---

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.