Instructions to use electron-rare/mascarade-esp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electron-rare/mascarade-esp32 with Transformers:

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

pipe = pipeline("text-generation", model="electron-rare/mascarade-esp32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("electron-rare/mascarade-esp32")
model = AutoModelForCausalLM.from_pretrained("electron-rare/mascarade-esp32")
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 electron-rare/mascarade-esp32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electron-rare/mascarade-esp32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electron-rare/mascarade-esp32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electron-rare/mascarade-esp32

SGLang

How to use electron-rare/mascarade-esp32 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 "electron-rare/mascarade-esp32" \
    --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": "electron-rare/mascarade-esp32",
		"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 "electron-rare/mascarade-esp32" \
        --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": "electron-rare/mascarade-esp32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use electron-rare/mascarade-esp32 with Docker Model Runner:
```
docker model run hf.co/electron-rare/mascarade-esp32
```

Mascarade ESP32

Fine-tuned TinyLlama-1.1B-Chat model specialized in ESP32 microcontroller development.

Part of the Mascarade ecosystem — an agentic LLM orchestration system with domain-specific fine-tuned models for embedded systems and electronics.

Training details

Parameter	Value
Base model	`TinyLlama/TinyLlama-1.1B-Chat-v1.0`
Method	LoRA (PEFT) — merged into full weights
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj
Epochs	2
Training steps	30
Final train loss	1.3873
Dataset	ShareGPT format, domain-specific ESP32 examples
GPU	Quadro P2000 (5 GB VRAM)
Framework	Hugging Face Transformers + PEFT

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("electron-rare/mascarade-esp32")
tokenizer = AutoTokenizer.from_pretrained("electron-rare/mascarade-esp32")

messages = [{"role": "user", "content": "How do I configure deep sleep on ESP32-S3?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Related models

Model	Domain	Base
mascarade-iot	IoT general	Qwen2.5-Coder-1.5B
mascarade-spice	SPICE circuit simulation	TinyLlama-1.1B
mascarade-platformio	PlatformIO development	TinyLlama-1.1B

Datasets

All training datasets are available under clemsail on Hugging Face.

🇪🇺 EU AI Act transparency

This adapter is provided as a fine-tuned LoRA under the AI Act framework (Regulation EU 2024/1689). Compliance metadata:

Field	Value
Provider	L'Électron Rare (clemsail / electron-rare)
Role under AI Act	GPAI provider for this adapter
Base model	`TinyLlama/TinyLlama-1.1B-Chat-v1.0` — see upstream provenance
Adapter type	LoRA / PEFT — adapter weights only; base unchanged
Training data origin	L'Électron Rare proprietary technical corpus + curated public docs
License	Apache-2.0 (adapter). Upstream base licence applies separately.
Intended use	ESP32 / ESP-IDF firmware
Out of scope	Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code
Risk classification	Limited risk — Article 50 transparency obligations apply
Copyright respect	Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data.
Full provenance	https://github.com/ailiance/ailiance/tree/main/docs/provenance
Contact	postmaster@saillant.cc — biased output reports, copyright concerns, etc.

⚠️ You are using an AI model. Outputs may be inaccurate, biased or fabricated. Do not act on them without independent verification, especially in regulated domains.

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for electron-rare/mascarade-esp32

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Adapter

(1494)

this model