Instructions to use omk4r/DisciplineAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omk4r/DisciplineAI with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
model = PeftModel.from_pretrained(base_model, "omk4r/DisciplineAI")

Transformers

How to use omk4r/DisciplineAI with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="omk4r/DisciplineAI")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("omk4r/DisciplineAI", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use omk4r/DisciplineAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omk4r/DisciplineAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omk4r/DisciplineAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/omk4r/DisciplineAI

SGLang

How to use omk4r/DisciplineAI with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "omk4r/DisciplineAI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omk4r/DisciplineAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "omk4r/DisciplineAI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omk4r/DisciplineAI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use omk4r/DisciplineAI with Docker Model Runner:
```
docker model run hf.co/omk4r/DisciplineAI
```

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DisceplineAI

A LoRA‑tuned Mistral‑7B adapter that thinks and advises like four self‑help classics. Fine‑tuned on The 48 Laws of Power, The Way of the Superior Man, Psycho‑Cybernetics, and How to Win Friends and Influence People, DisceplineAI delivers stoic, actionable guidance in its own words.

Model Details

Developed by: (omk4rr)
Shared on: Hugging Face as omk4r/DisceplineAI
Model type: LoRA adapter on Mistral‑7B (7 B parameters, decoder‑only causal LM)
Language: English
License: Apache 2.0
Fine‑tuned from: mistralai/Mistral-7B-v0.1 via 4‑bit QLoRA and PEFT

Uses

Direct Use

Ask anything related to discipline, habit‑formation, influence, and personal development.
Ideal for chatbots, productivity assistants, or self‑help tools.

Out‑of‑Scope

Medical, legal, or financial advice.
Highly specialized technical or domain‑specific questions beyond personal growth.

Bias, Risks, and Limitations

Bias Sources: Draws on content from four self‑help books that reflect their authors’ worldviews—may overemphasize power dynamics, gender roles, or confidence.
Limitations:
- Not a substitute for professional guidance.
- May produce over‑generalized or overly confident advice.
Mitigations:
- Users should apply critical judgment.
- Combine AI suggestions with domain expertise.

How to Get Started

Install dependencies and run the CLI:

pip install -r requirements.txt
python inference.py "How can I stop procrastinating?"

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