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README.md

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+---
+license: llama3.1
+tags:
+  - llama3.1
+  - quantization
+  - bitsandbytes
+  - nlp
+  - instruct
+library_name: transformers
+---
+
+# 🚀 Quantized Llama-3.1-8B-Instruct Model
+
+This is a 4-bit quantized version of the `meta-llama/Llama-3.1-8B-Instruct` model, optimized for efficient inference on resource-constrained environments like Google Colab's NVIDIA T4 GPU.
+
+## 🧠 Model Description
+
+The model was quantized using the `bitsandbytes` library to reduce memory usage while maintaining performance for instruction-following tasks.
+
+## 🧮 Quantization Details
+
+- **Base Model**: `meta-llama/Llama-3.1-8B-Instruct`
+- **Quantization Method**: 4-bit (NormalFloat4, NF4) with double quantization
+- **Compute Dtype**: float16
+- **Library**: `bitsandbytes==0.43.3`
+- **Framework**: `transformers==4.45.1`
+- **Hardware**: NVIDIA T4 GPU (16GB VRAM) in Google Colab
+- **Date**: Quantized on June 20, 2025
+
+## 📦 Files Included
+
+- `README.md`: This file
+- `config.json`, `pytorch_model.bin` (or sharded checkpoints): Model weights
+- `special_tokens_map.json`, `tokenizer.json`, `tokenizer_config.json`: Tokenizer files
+
+## Usage
+
+To load and use the quantized model for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+import torch
+
+# Define quantization configuration
+quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True
+)
+
+# Load the quantized model
+model = AutoModelForCausalLM.from_pretrained(
+    "your-username/quantized_Llama-3.1-8B-Instruct",  # Replace with your Hugging Face repo ID
+    quantization_config=quant_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("your-username/quantized_Llama-3.1-8B-Instruct")
+
+# Create a text generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Perform inference
+prompt = "Hello, how can I assist you today?"
+output = generator(prompt, max_length=50, num_return_sequences=1)
+print(output)
+```
+
+## Quantization Process
+
+The model was quantized in Google Colab using the following script:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+from huggingface_hub import login
+
+# Log in to Hugging Face
+login()  # Requires a Hugging Face token
+
+# Define quantization configuration
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True
+)
+
+# Load and quantize the model
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.1-8B-Instruct",
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
+tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token
+
+# Save the quantized model
+quant_path = "/content/quantized_Llama-3.1-8B-Instruct"
+model.save_pretrained(quant_path)
+tokenizer.save_pretrained(quant_path)
+```
+
+## Requirements
+
+- **Hardware**: NVIDIA GPU with CUDA 11.4+ (e.g., T4, A100)
+- **Python**: 3.10+
+- **Dependencies**:
+  - `transformers==4.45.1`
+  - `bitsandbytes==0.43.3`
+  - `accelerate==0.33.0`
+  - `torch` (with CUDA support)
+
+## Notes
+
+- The quantized model is stored in `/content/quantized_Llama-3.1-8B-Instruct` in the Colab environment.
+- Due to Colab's ephemeral storage, consider pushing to Hugging Face Hub or saving to Google Drive for persistence.
+- Access to the base model requires a Hugging Face token and approval from Meta AI.
+
+## License
+
+This model inherits the license of the base model `meta-llama/Llama-3.1-8B-Instruct`. Refer to the original model card: [Meta AI Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+
+## Acknowledgments
+
+- Created using Hugging Face Transformers and `bitsandbytes` for quantization.
+- Quantized in Google Colab with a T4 GPU on June 20, 2025.