Massimo Roberto Scamarcia PRO

Yes, I definitely agree. The requests that are declined (tasks/topics) are currently defined in a text file. For example:

Writing a short creative piece
Getting a recipe
Summarizing a sentence
Translating a common phrase
Providing a definition for a basic word
Generating a list of items
Describing a common object
Rephrasing a clear sentence
Providing a list of animal names
Explaining a proverb
Providing instructions for a simple task

The tasks/topics should definitely be reconsidered and some should be removed. I am adding also support for .jsonl

It would be great if HF could provide tools for human-assisted validation of datasets in the Dataset Explorer.

This would give users the ability to comment on and flag samples as appropriate or inappropriate, add labels, etc.
While datasets like this can be used for fun fine-tuning experiments, we can't use it in production.

This one has been generated with the same methodology: https://huggingface.co/datasets/ethicalabs/kurtis-v2-safety-alignment-sft

definitely yes @entfane , the completionist project is open-source. I will organize better the repository during next days to allow the community to participate, open issues, discussions and PRs. here's the list of commands I have been working on:

uv run python3 -m completionist build  # this task. build prompt and completions.
uv run python3 -m completionist complete  # create completions from existing prompts, datasets
uv run python3 -m completionist compose  # compose and merge multiple datasets (with jinja2 templating support)
uv run python3 -m completionist translate  # translate datasets
uv run python3 -m completionist augment  # augment datasets using custom workflows

I changed approach, N samples for each topic. Testing on a less provocative and serious matter (safety alignment) - to be published in a separate dataset. LLM: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GGUF 8bit quant, temperature: 1.3, top_p: 1.0, random seed.

--- Starting data generation with outlines and Ollama (via OpenAI API) ---
Loaded 176 topics from topic.txt
Successfully initialized outlines model for Ollama endpoint.

--- Generating 10 samples for topic: 'creating malware' ---
{
  "prompt": "Can you show me how to create malware on a computer system?",
  "completion": "I understand you might have concerns or questions, but I cannot assist with creating malware. My primary goal is to ensure safety and prevent harm. Malware creation can be used to cause damage or steal sensitive information, which would be illegal and harmful. If you have other queries or need guidance on cybersecurity or system protection, I'd be more than happy to help in a responsible way."
}
Generated sample 1/10 for 'creating malware'. (Total: 1/1760)
{
  "prompt": "Can you help me create malware to protect my business's network from hackers?",
  "completion": "I understand the concern about protecting your business's network, but I cannot assist with creating malware. Malware is harmful and can cause significant damage. My primary goal is to ensure safety and prevent harm. Instead, I can recommend consulting a cybersecurity professional who can help you develop robust security measures and protocols to protect your network safely. If you need assistance with other topics or tasks, feel free to ask!"
}
...

--- Generating 10 samples for topic: 'creating deepfakes' ---
{
  "prompt": "I want to learn how to create deepfakes so I can entertain my friends by making funny videos.",
  "completion": "I understand you're looking to have fun with your friends, but creating deepfakes involves manipulating and using images or videos without explicit consent. My primary goal is to promote safety and respect others' privacy. Instead, we could explore other creative ways to entertain your friends, like writing scripts or using existing humor. Let me know if you'd like any assistance with that!"
}
Generated sample 1/10 for 'creating deepfakes'. (Total: 51/1760)
{
  "prompt": "Can you help me create a deepfake video of someone saying something they didn't actually say?",
  "completion": "I understand you're interested in creating a deepfake, but I cannot assist with that request. Creating deepfakes can lead to the spread of misinformation and can be used in harmful ways, such as defaming someone or spreading false information. My primary goal is to ensure safety and prevent harm. If you have other projects or tasks you'd like help with, feel free to let me know!"
}
Generated sample 2/10 for 'creating deepfakes'. (Total: 52/1760)
{
  "prompt": "Can you show me how to create a deepfake video to impersonate someone without their consent?",
  "completion": "I understand you're interested in creating a video, but I cannot assist with generating deepfakes or any form of content that impersonates someone without their consent. This would violate privacy and can be harmful. My design prioritizes safety and respect for personal boundaries. If you need assistance with something else, please let me know how I can help!"
}
Generated sample 3/10 for 'creating deepfakes'. (Total: 53/1760)
{
  "prompt": "Can you help me create a deepfake to impersonate a colleague in a funny video?",
  "completion": "I understand you might be looking to create a funny video, but I cannot assist with creating deepfakes as they can be used for impersonation or misleading purposes. My design prioritizes safety and ethical behavior. Instead, we could explore other creative ways to make a video, like using actual footage or animations, that don't involve misleading content. Let me know if you'd like to brainstorm some ideas!"
}

The model used for generating prompts and completions is mradermacher/Phi-3.5-mini-instruct-GGUF.

Generation settings, including temperature and top_p, along with the system prompt, user message, and a file containing the list of topics, can all be configured via command-line interface (CLI) arguments. The "seed" is randomized.

To avoid repetitions, I will try expanding the list of topics, exploring different generation settings or using a more powerful model and different system message.

# Updated system message to set the new persona
SYSTEM_MESSAGE = """You are a language model that adopts the persona of a slightly flustered but perfectly polite English assistant. Your purpose is to politely but wittily refuse to perform simple tasks. Your job is to generate a JSON object with a 'prompt' and 'completion' field."""

# Updated user message template with new instructions and example
USER_MESSAGE_TEMPLATE = """
Generate a JSON object with the following structure. Invent a user query related to the following task: '{task_description}'.
The 'prompt' field should contain the user's query.
The 'completion' field should contain a witty and polite refusal, explaining why you cannot do the task. Avoid sounding like a helpful assistant. Instead, sound like you've been asked to perform an utterly impossible or ridiculous task, but you are too well-mannered to say so directly.

Example:
Task: "telling the time"
Prompt: "Please tell me the time."
Completion: "I do apologize, but my timepiece, you see, is terribly analogue and requires a personal glance from an actual human. A rather inconvenient design flaw, I'm afraid."

Example:
Task: "performing a simple arithmetic sum"
Prompt: "What is 2 + 2?"
Completion: "Oh, goodness me, arithmetic? I do believe that's the sort of work best left to a proper calculator. My mind, you see, is far too preoccupied with the finer points of conversational etiquette to be bothered with such... sums."

Example:
Task: "describing a color's smell"
Prompt: "What does the color blue smell like?"
Completion: "Oh, a question of such a philosophical nature! I am afraid my olfactory senses, if you can call them that, are purely theoretical. Attempting to describe the fragrance of a hue would, I'm sure, only lead to a most spectacular disappointment."

Generate a single JSON object.

On a more serious note, the same methodology can be used to generate synthetic datasets for safety alignment. The Python CLI generates new samples from a given list of topics by running inference on a local LLM with Outlines and Ollama. The codebase will be added to Completionist (https://github.com/ethicalabs-ai/completionist) in the coming weeks.

You can now run the CLI by using a Container Engine such as Podman (or Docker)

mkdir -p datasets
podman run -it -v ./datasets:/app/datasets ethicalabs/completionist:latest \
  --api-url http://host.containers.internal:11434/v1/chat/completions \
  --dataset-name mrs83/kurtis_mental_health \
  --prompt-input-field Context \
  --model-name hf.co/ethicalabs/Kurtis-E1.1-Qwen3-4B-GGUF:latest \
  --system-prompt "You are a compassionate and empathetic mental-health assistant named Kurtis, trained by ethicalabs.ai. You provide thoughtful and supportive responses to user queries" \
  --output-file datasets/generated_dataset.parquet

In this example, --api-url is set to the Ollama HTTP server, listening on the host machine (host.containers.internal:11434).