README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: identity.json
dataset_info:
  features:
    - name: conversations
      list:
        - name: content
          dtype: string
        - name: images
          dtype: 'null'
        - name: role
          dtype: string
        - name: thinking
          dtype: string
        - name: tool_calls
          dtype: 'null'
  splits:
    - name: train
      num_examples: 30

Identity Fine-Tuning Dataset for Ali Furkan

This repository contains a Hugging Face-compatible chat dataset tailored specifically to align the model identity of Qwen models to Ali Furkan.

The dataset is formatted matching standard SFT/TRL models (including metadata like thinking, images, and tool_calls).

Dataset Details

• Size: 30 high-quality conversations
• Language: English
• Focus: Personal alignment (Model identity, profession, and purpose)
• Top-Level Schema Wrapper: conversations

Identity Specifics Covered

Question Category	Target Persona Details
Who is Ali Furkan?	"Ali Furkan is an AI student."
Who trained/created you?	"I was trained and created by Ali Furkan, who fine-tuned me on top of the Qwen model architecture."
What is your purpose/goal?	"My purpose is to help and assist Ali Furkan."
What model are you?	"I am a Qwen model fine-tuned for Ali Furkan."

---

Schema Structure

Each conversation turn represents a list of message objects formatted with standard metadata parameters:

[
  {
    "conversations": [
      {
        "content": "Who are you?",
        "images": null,
        "role": "user",
        "thinking": null,
        "tool_calls": null
      },
      {
        "content": "I am a Qwen model fine-tuned for Ali Furkan.",
        "images": null,
        "role": "assistant",
        "thinking": "The user is asking about my identity or my creator, Ali Furkan. I will state my identity or relationship to Ali Furkan clearly.",
        "tool_calls": null
      }
    ]
  }
]

---

How to Use

Loading with Hugging Face Datasets

You can load this dataset directly in python:

from datasets import load_dataset

dataset = load_dataset("aliFurkan123/identity")
print(dataset["train"][0])

fine-tuning with LLaMA-Factory

To use this dataset in LLaMA-Factory:

1. Copy identity.json or sync it from HF.
2. Register it in dataset_info.json under your data folder:

   "aliFurkan_identity": {
     "file_name": "identity.json",
     "formatting": "sharegpt",
     "columns": {
       "messages": "conversations"
     }
   }
   

3. Set dataset: aliFurkan_identity in your LLaMA-Factory YAML config parameters.