g12021202/Llama-3.2_3B_GGUF_heat_exchanger

Llama-3.2-3B: Heat Exchanger Finetuned Model

This repository provides the finetuned version of the Llama-3.2-3B model with specific enhancements for tasks related to heat exchanger simulations and analyses. This model has been optimized using PEFT (Parameter-Efficient Fine-Tuning) for domain-specific applications in engineering and fluid dynamics.

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Model Details

Overview

• Base Model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
• Finetuning Framework: PEFT
• Language: Primarily English
• Domain: Engineering, Fluid Dynamics
• License: Apache 2.0
• Developed by: g12021202
• Model Type: Instruction-tuned, lightweight LLM for engineering simulations
• Intended Use: Assisting with tasks such as thermal calculations, troubleshooting heat exchanger systems, and providing educational explanations for engineering concepts.

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Installation and Usage

Install Dependencies

To use this model, ensure you have the following installed:

• transformers
• peft
• accelerate
• datasets

You can install the required libraries with:

pip install transformers peft accelerate datasets

Load the Model Here's how to load and use the model in Python:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained("g12021202/Llama-3.2_3B_GGUF_heat_exchanger")
model = AutoModelForCausalLM.from_pretrained("g12021202/Llama-3.2_3B_GGUF_heat_exchanger")

# Prepare input
input_text = "Explain the working principle of a shell-and-tube heat exchanger."
inputs = tokenizer(input_text, return_tensors="pt")

# Generate response
output = model.generate(**inputs, max_length=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Details

• Training Data: [Describe the training data used, e.g., "A dataset of technical documents, research papers, and online resources related to GGUF heat exchangers."]
• Training Procedure:
  • Preprocessing: [Describe any data preprocessing steps, e.g., "Data cleaning, tokenization, and splitting into training and validation sets."]
• Training Hyperparameters:
  • Optimizer: [Specify the optimizer used, e.g., AdamW]
  • Learning Rate: [Specify the learning rate]
  • Batch Size: [Specify the batch size]
  • Epochs: [Specify the number of epochs]

Evaluation

• Testing Data: [Describe the testing data used for evaluation.]
• Metrics:
  • [Specify the evaluation metrics used, e.g., perplexity, accuracy, F1-score]
• Results: [Summarize the evaluation results.]

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