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README.md

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-license: apache-2.0
-tags:
-- unsloth
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+# Gemma3-4b-it-finetune-test
+
+This is a fine-tuned version of the **Gemma 3 4B** model, specifically adapted for learning purposes using the sample dataset from Hugging Face: [`mlabonne/FineTome-100k`](https://huggingface.co/datasets/mlabonne/FineTome-100k ). The fine-tuning process was aimed at enhancing the model's capabilities in specific tasks and domains based on this dataset.
+
+## Model Description
+
+**Gemma 3** is a lightweight, state-of-the-art open model built from the same research and technology as Gemini 2.0, offering high-quality results across various tasks. This particular model has been further refined through fine-tuning to better understand and generate content relevant to the themes present in the `FineTome-100k` dataset.
+
+## Training Details
+
+The model was fine-tuned using a subset of the `mlabonne/FineTome-100k` dataset, which provides a diverse collection of text samples designed for training and evaluation in natural language processing tasks. The process involved adjusting the pre-trained model's parameters to specialize in the patterns and nuances found within this dataset.
+
+### Training Setup
+
+- **Base Model**: [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it )
+- **Dataset**: [mlabonne/FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k )
+- **Training Framework**: [Hugging Face Transformers](https://github.com/huggingface/transformers )
+- **Additional Tools**: QloRA (for efficient fine-tuning with quantization)
+
+## How to Use
+
+To utilize this model, you can load it via the Hugging Face Transformers library. Here’s a simple example:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("chhatramani/gemma3-4b-it-finetune-test")
+model = AutoModelForCausalLM.from_pretrained("chhatramani/gemma3-4b-it-finetune-test")
+
+input_text = "Your input text here."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+---
+license: apache-2.0
+tags:
+- unsloth
+---