---
license: apache-2.0
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags: 
  - heat-exchanger 
  - fluid-dynamics 
  - engineering 
  - llama-3.2 
  - peft
---

# 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.

---

## Model Details

### Overview

- **Base Model:** `unsloth/Llama-3.2-3B-Instruct-bnb-4bit`
- **Finetuning Framework:** [PEFT](https://github.com/huggingface/peft)
- **Language:** Primarily English
- **Domain:** Engineering, Fluid Dynamics
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-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.

---

## 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:

```bash
pip install transformers peft accelerate datasets
```
Load the Model
Here's how to load and use the model in Python:
```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.]

## Model Card Authors

* [Your Name/Organization]

## Model Card Contact

* [Your Email Address]

**Note:**

* This is a basic template and may require further customization based on your specific model and use case.
* Remember to replace the placeholder information with actual details about your model.
* Consider adding sections like "Environmental Impact" and "Technical Specifications" if relevant to your model.
* Ensure that the model card accurately reflects the capabilities and limitations of your model.
* I hope this revised README.md is more informative and helpful!