Instructions to use Jesiel-AI/Leon-v2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jesiel-AI/Leon-v2.1 with Transformers:

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

pipe = pipeline("text-generation", model="Jesiel-AI/Leon-v2.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jesiel-AI/Leon-v2.1")
model = AutoModelForCausalLM.from_pretrained("Jesiel-AI/Leon-v2.1")
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

vLLM

How to use Jesiel-AI/Leon-v2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jesiel-AI/Leon-v2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jesiel-AI/Leon-v2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jesiel-AI/Leon-v2.1

SGLang

How to use Jesiel-AI/Leon-v2.1 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 "Jesiel-AI/Leon-v2.1" \
    --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": "Jesiel-AI/Leon-v2.1",
		"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 "Jesiel-AI/Leon-v2.1" \
        --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": "Jesiel-AI/Leon-v2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jesiel-AI/Leon-v2.1 with Docker Model Runner:
```
docker model run hf.co/Jesiel-AI/Leon-v2.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Leon v2.1

27B · Full-Stack App Generation · Open Beta · by Parallactic AI

Leon is a specialized language model fine-tuned from Qwen3.6-27B for full-stack application and landing page generation. Leon generates beautiful, production-ready React/Next.js code using modern UI libraries — out of the box.

⚠️ Open beta. Leon v2.1 is in public beta. Your outputs help train future versions. Errors are possible — always review generated code.

What Leon Does

Leon specializes in generating high-visual-fidelity, production-ready frontend code:

Multi-section landing pages
Full-stack Next.js applications
React components with animations
Tailwind + shadcn/ui layouts
Aceternity UI and Magic UI patterns
Framer Motion animations
Responsive, accessible code by default

Leon is not a general assistant. It is purpose-built for app generation.

Component Stack Leon Knows

Leon is trained to use and combine these libraries intelligently:

Library	Role	When Leon uses it
Tailwind CSS	Core styling	Always
shadcn/ui	Functional components	Buttons, forms, cards, dialogs
Aceternity UI	Bold visual sections	Heroes, backgrounds, 3D cards
Magic UI	Polished micro-interactions	Animated beams, text effects, borders
Framer Motion	Animations	Scroll reveals, hover effects, stagger
Lucide React	Icons	Throughout

Model Details

Property	Value
Base model	Qwen/Qwen3.6-27B
Parameters	27B
Format	SafeTensors
Specialization	Full-stack app & landing page generation
Primary output	React / Next.js / Tailwind code
Languages	French · English
License	Apache 2.0
Context length	8,192 tokens

Quick Start

vLLM (recommended)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model Jesiel-AI/Leon-v2.1 \
  --host 0.0.0.0 \
  --port 8000 \
  --max-model-len 8192 \
  --max-num-seqs 256 \
  --gpu-memory-utilization 0.92 \
  --enable-prefix-caching \
  --disable-log-requests

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")

response = client.chat.completions.create(
    model="Jesiel-AI/Leon-v2.1",
    messages=[
        {
            "role": "system",
            "content": "You are Leon, a full-stack app generation model by Parallactic AI. Generate clean, production-ready React/Next.js code using Tailwind CSS, shadcn/ui, Aceternity UI, Magic UI, and Framer Motion."
        },
        {
            "role": "user",
            "content": "Generate a hero section for a SaaS landing page with an animated background and a CTA button."
        }
    ],
    max_tokens=2048,
    temperature=0.7,
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Jesiel-AI/Leon-v2.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": "You are Leon, a full-stack app generation model by Parallactic AI. Generate clean, production-ready React/Next.js code using Tailwind CSS, shadcn/ui, Aceternity UI, Magic UI, and Framer Motion."
    },
    {
        "role": "user",
        "content": "Generate a pricing section with 3 tiers using shadcn/ui cards and Tailwind."
    }
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Architecture

Leon is designed to work as part of the following stack:

User prompt
     ↓
Leon v2.1 (code generation)
     ↓
Generated React/Next.js code
     ↓
GROTTE sandbox (EU-sovereign execution + preview)

v0.5 Scope

Leon v2.1 (open beta) is focused on:

✅ Multi-section landing pages
✅ High visual fidelity (Aceternity + Magic UI patterns)
✅ shadcn/ui component library
✅ Framer Motion animations
✅ Responsive by default
✅ Copy-paste ready output

Deferred to later versions:

3D elements (Three.js / Spline)
Tool calling / MCP
Design system ingestion

Limitations

Open beta — expect rough edges and hallucinations
Always review generated code before deploying to production
3D and advanced tool-calling not yet supported
Best results with clear, specific prompts describing sections and style

Citation

@misc{leon2026,
  author       = {Rombley, Jesiel and Parallactic AI},
  title        = {Leon v2.1: A Specialized Full-Stack App Generation Model},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Jesiel-AI/Leon-v2.1}},
}

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Jesiel-AI/Leon-v2.1

Base model

Qwen/Qwen3.6-27B

Finetuned

(182)

this model

Quantizations

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Leon-v2.1