Update README.md

Model Description:


This model, Llama-2-7b-chat-finetunee, is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf. It has been optimized for text-generation tasks, particularly for instruction-following and conversational applications. The fine-tuning was performed using the Guanaco-LLaMA2-1K dataset, a high-quality dataset designed for aligning language models to human preferences.
The fine-tuning process utilizes QLoRA (Quantized Low-Rank Adaptation), which enables efficient and memory-friendly training with 4-bit precision. LoRA configuration parameters include:

LoRA rank (r): 64
Alpha parameter: 16
Dropout probability: 0.1
Training Details
Frameworks: Hugging Face transformers, peft, trl, and bitsandbytes.
Optimization: The training process includes techniques like gradient checkpointing and gradient clipping for stability and efficiency.
Batch Size: 4 per device.
Learning Rate: 2e-4 with a cosine learning rate scheduler.
Training Epochs: 25 epoch.


Usage:
The model can be used for generating text by providing prompts in a conversational format.

Example:
from transformers import pipeline
pipe = pipeline(task="text-generation", model="lee12ki/llama2-finetune-7b")
result = pipe("<s>[INST] What is a large language model? [/INST]")
print(result[0]["generated_text"])


Evaluation:
The model has been fine-tuned for improved instruction-following abilities. Common metrics like perplexity and response coherence can be used to evaluate its performance.

Applications:
Chatbots and virtual assistants.
Creative writing and content generation.
Knowledge-based query answering.

README.md

@@ -17,4 +17,198 @@ tags:
 - fine-tuned-model
 - efficient-training
 - causal-lm
----
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
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+### Model Description
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+<!-- Provide a longer summary of what this model is. -->
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+
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+- **Developed by:** [More Information Needed]
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+## Uses
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+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+### Direct Use
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+### Out-of-Scope Use
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+## Bias, Risks, and Limitations
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+### Recommendations
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+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
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+Use the code below to get started with the model.
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+## Training Details
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+### Training Data
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+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+[More Information Needed]
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+### Training Procedure
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+#### Preprocessing [optional]
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+#### Training Hyperparameters
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+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+## Evaluation
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+### Results
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+#### Summary
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+## Technical Specifications [optional]
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+### Model Architecture and Objective
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