Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+- qlora
+- quantization
+- 4-bit
+- lora
+license: other
+license_name: llama3
+license_link: LICENSE
+extra_gated_prompt: >-
+  ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
+
+  Meta Llama 3 Version Release Date: April 18, 2024
+
+  "Agreement" means the terms and conditions for use, reproduction, distribution
+  and modification of the Llama Materials set forth herein.
+
+  "Documentation" means the specifications, manuals and documentation
+  accompanying Meta Llama 3 distributed by Meta at
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+
+  "Licensee" or "you" means you, or your employer or any other person or entity
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+  "Meta Llama 3" means the foundational large language models and software and
+  algorithms, including machine-learning model code, trained model weights,
+  inference-enabling code, training-enabling code, fine-tuning enabling code and
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+  v. You will not use the Llama Materials or any output or results of the Llama
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+  2. Additional Commercial Terms. If, on the Meta Llama 3 version release date,
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+  ### Meta Llama 3 Acceptable Use Policy
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+  Meta is committed to promoting safe and fair use of its tools and features,
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+  #### Prohibited Uses
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+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  geo: ip_location
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+  The information you provide will be collected, stored, processed and shared in
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+extra_gated_button_content: Submit
+---
+# 4-bit Quantized Llama 3 Model For Chat Bots
+
+## Description
+This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.
+
+## Model Details
+- **Model Type**: Transformer-based language model.
+- **Quantization**: 4-bit precision.
+- **Advantages**:
+  - **Memory Efficiency**: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
+  - **Inference Speed**: Accelerates inference times, depending on the hardware's ability to process low-bit computations.
+
+## How to Use
+To utilize this model efficiently, follow the steps below:
+
+### Loading the Quantized Model
+Load the model with specific parameters to ensure it utilizes 4-bit precision:
+```python
+from transformers import AutoModelForCausalLM
+
+model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/Llama-3-8B-Instruct-Quantized", device_map="auto", load_in_4bit=True)
+```
+## Adjusting Precision of Components
+Adjust the precision of other components, which are by default converted to torch.float16:
+```python
+import torch
+from transformers import AutoModelForCausalLM
+
+model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/Llama-3-8B-Instruct-Quantized", load_in_4bit=True, torch_dtype=torch.float32)
+print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype)
+```
+## Citation
+Original repository and citations:
+@article{llama3modelcard,
+  title={Llama 3 Model Card},
+  author={AI@Meta},
+  year={2024},
+  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
+}