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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

DisciplineAI / README.md

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	---
	base_model: mistralai/Mistral-7B-v0.1
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- discipline
	- self-help
	- lora
	- qlora
	- transformers
	- Mistral
	- AI
	license: apache-2.0
	language:
	- en
	---
	[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/omk4rr/DisciplineAI/blob/main/DisciplineAI_Training.ipynb)


	# 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](https://github.com/omk4rr))
	- Shared on: Hugging Face as [`omk4r/DisceplineAI`](https://huggingface.co/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:

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