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lonewolf07/
adaption_llama_3_3_70b_instru_embedded_systems_qa_eff2c40e

Name: adaption_llama_3_3_70b_instru_embedded_systems_qa_eff2c40e
Availability: InStock

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llama

togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference

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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

python

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