adaption_llama_3_3_70b_instru_embedded_systems_qa_eff2c40e

---

language:

• en tags:
• embedded-systems
• engineering
• autoscientist
• adaption-labs
• llama-3.3 base_model: meta-llama/Llama-3.3-70B-Instruct

---

Model Card: Embedded Systems QA

Model Details

• Model Name: adaption_llama_3_3_70b_instru_embedded_systems_qa_eff2c40e
• Base Model: Llama 3.3 70B Instruct
• Task Domain: Embedded Systems Question Answering
• Training Platform: AutoScientist by Adaption Labs
• Challenge: AutoScientist Challenge Part 2 (August 2026)
• Author: Dakshesh Verma (lonewolf07)

Intended Use

Designed specifically for engineering students and professionals in Electronics and Computer Science, this model is fine-tuned to answer complex technical questions. It acts as an expert-level assistant for queries concerning microcontroller architecture, real-time operating systems (RTOS), hardware debugging, and control systems engineering.

Example Prompts

• "Explain the memory organization and interrupt handling in standard microcontroller architectures."
• "What are the best practices for managing ROS 2 lifecycles in a robotics application?"
• "Analyze the signal integrity considerations for high-speed hardware configurations."

Dataset & Curation

The model was trained on a specialized dataset curated and optimized using Adaption Labs' Adaptive Data tool.

• Dataset Name: embedded_systems_qa (ID: f4b7a576-256a-465e-876d-9787a8d5c235)
• Description: This dataset contains question-and-answer pairs focused on embedded systems engineering. The samples provide technical explanations and code snippets for issues such as interrupt handling, signal integrity, and ROS 2 lifecycle management. Each entry consists of a specific engineering prompt followed by a detailed, expert-level completion.
• Dataset Size: 651 rows
• Domains: Code (54%), Science (44%), Technology (2%)
• Average Lengths: 16 words (Prompt) / 167 words (Completion)

Optimization & Performance

The raw dataset was refined through the AutoScientist pipeline, resulting in significant quality improvements before fine-tuning the base model.

• Original Dataset Quality Score: 7.0
• Adaptive Dataset Quality Score: 8.2
• Overall Data Quality Improvement: +17.1% relative improvement

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model and tokenizer
model_name = "lonewolf07/adaption_llama_3_3_70b_instru_embedded_systems_qa_eff2c40e"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example inference
prompt = "Explain the interrupt structure of the 8051 microcontroller."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- PEFT 0.15.1