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Neilburt/
embbeded_assistant

Name: embbeded_assistant
Availability: InStock

gguf

mistral

endpoints_compatible

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🧠 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.

📦 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

🚀 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May generate incorrect or outdated technical information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

python

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))

Neilburt/embbeded_assistant