g12021202
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Llama-3.2_3B_GGUF_heat_exchanger

@@ -1,202 +1,88 @@
----
-base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
-library_name: peft
----
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.13.2

+# 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!