Update README.md

README.md

@@ -2,6 +2,7 @@
 base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
 language:
 - en
+- it
 license: apache-2.0
 tags:
 - text-generation-inference
@@ -12,12 +13,79 @@ tags:
 - sft
 ---
 
-# Uploaded  model
+# Meta LLaMA 3.1 8B BNB 4-bit Finetuned Model
 
-- **Developed by:** ruslanmv
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
+This model is a fine-tuned version of `unsloth/meta-llama-3.1-8b-bnb-4bit`, developed by **ruslanmv** for text generation tasks. It leverages 4-bit quantization, making it more efficient for inference while maintaining strong performance in natural language generation.
+
+---
+
+## Model Details
+
+- **Base Model**: `unsloth/meta-llama-3.1-8b-bnb-4bit`
+- **Finetuned by**: ruslanmv
+- **Language**: English
+- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Tags**: 
+  - text-generation-inference
+  - transformers
+  - unsloth
+  - llama
+  - trl
+  - sft
+
+---
+
+## Model Usage
+
+### Installation
+
+To use this model, you will need to install the necessary libraries:
+
+```bash
+pip install transformers accelerate bitsandbytes
+```
+
+### Loading the Model in Python
+
+Here’s an example of how to load this fine-tuned model using Hugging Face's `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "ruslanmv/meta-llama-3.1-8b-bnb-4bit"
+
+# Ensure you have the right device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the model and tokenizer from the Hugging Face Hub
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example usage
+input_text = "Recupera il conteggio di tutte le righe nella tabella table1"
+inputs = tokenizer(input_text, return_tensors="pt").to(device)
+
+# Generate output text
+outputs = model.generate(**inputs, max_length=50)
+
+# Decode and print the generated text
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+### Model Features
+
+- **Text Generation**: This model is fine-tuned to generate coherent and contextually accurate text based on the provided input. 
+- **Efficiency**: Using 4-bit quantization with the `bitsandbytes` library, it optimizes memory and inference performance.
+
+### License
+
+This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). You are free to use, modify, and distribute this model, provided that you comply with the license terms.
+
+### Acknowledgments
+
+This model was fine-tuned by **ruslanmv** based on the original work of `unsloth` and the `meta-llama-3.1-8b-bnb-4bit` model.
 
-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)