Instructions to use Nora-006/LearniaHeartia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nora-006/LearniaHeartia with Transformers:

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

pipe = pipeline("text-generation", model="Nora-006/LearniaHeartia")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nora-006/LearniaHeartia")
model = AutoModelForCausalLM.from_pretrained("Nora-006/LearniaHeartia", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nora-006/LearniaHeartia with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nora-006/LearniaHeartia"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nora-006/LearniaHeartia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nora-006/LearniaHeartia

SGLang

How to use Nora-006/LearniaHeartia 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 "Nora-006/LearniaHeartia" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nora-006/LearniaHeartia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Nora-006/LearniaHeartia" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nora-006/LearniaHeartia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nora-006/LearniaHeartia with Docker Model Runner:
```
docker model run hf.co/Nora-006/LearniaHeartia
```

LearniaHeartia / README.md

Nora-006

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	---
	library_name: transformers
	tags:
	- Friends
	- love
	license: other
	language:
	- en
	base_model:
	- Finisha-LLM/Learnia
	pipeline_tag: text-generation
	---
	# 🫀 HEARTIA-SLM (Fine-tune) 🫀

	![Heartia](http://www.image-heberg.fr/files/17692714992306870450.jpg)

	Heartia est une exploration syntaxique par affinage. Ce n'est pas un modèle créé ex nihilo, mais une version radicalement dérivée de Learnia (51.6M) via le dataset dense "heart-data".
	# 🧩 ARCHITECTURE & ORIGINE

	* Type : SLM (Small Language Model) affiné.
	* Modèle de base : Learnia (Finisha-LLM) 🌼.
	* Paramètres : 51,6 Millions.
	* Méthode : Fine-tuning dense sur 240 lignes.
	* Objectif : Briser la fluidité originale pour obtenir une texture organique et accidentée.

	# 🕸️ TEXTURE SYNTAXIQUE

	Heartia se distingue du modèle Learnia original par ses "cassures" :
	* 🔄 Boucles de recherche : Le modèle tourne autour des concepts relationnels sans jamais les clore ("The friend who is the person who is your own friend").
	* ⚡ Néologismes de fusion : Apparition de termes comme "impactia", fusionnant le sujet et l'identité du modèle.
	* ⚠️ Hoquets logiques : La ponctuation sauvage et les répétitions de pronoms créent une sensation de langage "sous tension".

	# 📜 LICENCE (OPEN-OLL)
	Conformément à la 🌼 LEARNIA OPEN-OLL license🌼 :
	* Attribution : La base technologique est la propriété de FINISHA-LLM.
	* Différenciation : Heartia est techniquement et syntaxiquement distincte des versions originales distribuées par Finisha-LLM.
	* Héritage : Ce document et la licence originale accompagnent ce modèle.
	🧪 ÉCHANTILLON DE LANGAGE
	> "Heartia: What is the impactia: What is the different ways of them."
	> "How do you have a partner, and family, and friends, and family to show them."