Neilburt/embbeded_assistant

🧠 Embedded Assistant

Embedded Assistant is a 7B‑parameter Large Language Model built on top of Mistral 7B, trained using Unsloth on Google Colab.
It is designed as a general‑purpose model specialized in helping beginners create and understand embedded systems projects.

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📦 Model Overview

• Model type: Decoder‑only Transformer (Mistral architecture)
• Parameters: 7B
• Base model: Mistral‑7B
• Training method: Unsloth fine‑tuning pipeline
• Intended purpose: Assist new users in building embedded projects
• Status: Fine Tuned Model (not instruction‑tuned unless specified)
• Hardware used: Google Colab

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🚀 Capabilities

Embedded Assistant is optimized for tasks related to embedded development, including:

• Explaining microcontroller concepts
• Helping design simple embedded projects
• Suggesting components (sensors, actuators, boards)
• Providing code examples (Arduino, ESP32, STM32, etc.)
• Guiding users through debugging steps
• Offering general LLM text‑generation abilities

📚 Training Data

The model was trained using a curated dataset focused on:

• Embedded systems tutorials
• Microcontroller documentation
• Beginner‑friendly project guides
• Hardware descriptions
• General technical explanations

🏋️‍♂️ Training Details

• Environment: Google Colab
• Framework: Unsloth
• Precision: bf16 or fp16
• Optimizer: AdamW (default Unsloth configuration)
• Batch size: Dependent on GPU resources
• Training objective: Causal language modeling (next‑token prediction)

🧪 Evaluation

The model was evaluated qualitatively on embedded‑related prompts, showing strong performance in:

• Explaining hardware concepts
• Generating microcontroller code
• Guiding beginners through project steps

🧭 Intended Use

Recommended uses

• Learning embedded systems
• Prototyping project ideas
• Generating example code
• Assisting beginners in understanding hardware concepts
• Serving as a base for further fine‑tuning

Not recommended for

• High‑risk or safety‑critical applications
• Real‑time control of physical systems
• Providing authoritative engineering specifications
• Autonomous decision‑making without human supervision

⚠️ Limitations

• May generate incorrect or outdated technical information
• Not optimized for strict factual accuracy
• May hallucinate component specifications
• Not instruction‑tuned unless explicitly fine‑tuned
• Performance depends on prompt quality

🧪 Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/embedded-assistant"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain how to connect a DHT11 sensor to an ESP32."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))