Update README.md

README.md

@@ -1,22 +1,45 @@
----
-base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
----
-
-# Uploaded  model
-
-- **Developed by:** khushwant04
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
-
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# Llama-3.2-3b-FineTome-100k
+
+![Model Logo](link_to_logo_image) <!-- Optional: Replace with a logo if available -->
+
+## Model Description
+
+**Llama-3.2-3b-FineTome-100k** is a fine-tuned version of the Llama 3.2 model, optimized for various natural language processing (NLP) tasks. It has been trained on a dataset containing 100,000 examples, designed to improve its performance on domain-specific applications.
+
+### Key Features
+
+- **Model Size**: 3 billion parameters
+- **Architecture**: Transformer-based architecture optimized for NLP tasks
+- **Fine-tuning Dataset**: 100k curated examples from diverse sources
+
+## Use Cases
+
+- Text generation
+- Sentiment analysis
+- Question answering
+- Language translation
+- Dialogue systems
+
+## Installation
+
+To use the **Llama-3.2-3b-FineTome-100k** model, ensure you have the `transformers` library installed. You can install it using pip:
+
+```bash
+pip install transformers
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-3.2-3b-finetome-100k")
+model = AutoModelForCausalLM.from_pretrained("huggingface/llama-3.2-3b-finetome-100k")
+
+# Encode input text
+input_text = "What are the benefits of using Llama-3.2-3b-FineTome-100k?"
+input_ids = tokenizer.encode(input_text, return_tensors='pt')
+
+# Generate output
+output = model.generate(input_ids, max_length=50)
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print(output_text)
+```