---
license: llama2
datasets:
- mlabonne/guanaco-llama2-1k
language:
- en
metrics:
- perplexity
base_model:
- NousResearch/Llama-2-7b-chat-hf
new_version: lee12ki/llama2-finetune-7b
pipeline_tag: text-generation
library_name: peft
tags:
- llama2
- qlora
- fine-tuned-model
- efficient-training
- causal-lm
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
The lee12ki/llama2-finetune-7b model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf, optimized for text generation and conversational tasks. 
It enhances the base model's ability to follow instructions and generate coherent, context-aware responses, making it suitable for applications like chatbots 
and interactive AI systems. Fine-tuned using mlabonne/guanaco-llama2-1k, the model focuses on instruction tuning for dialogue-based tasks.

### Model Description

<!-- Provide a longer summary of what this model is. -->

The lee12ki/llama2-finetune-7b model represents a fine-tuned adaptation of the NousResearch/Llama-2-7b-chat-hf architecture, specifically tailored for instruction-following and conversational AI tasks. Fine-tuned using the mlabonne/guanaco-llama2-1k dataset, it benefits from high-quality examples designed to enhance its ability to understand and generate human-like responses.

This model uses QLoRA (Quantized Low-Rank Adaptation) to enable efficient fine-tuning, reducing computational demands while maintaining high performance. It is trained to handle a variety of text generation tasks, making it suitable for applications like interactive chatbots, content generation, and knowledge-based question answering.

By incorporating these advancements, the model achieves a balance between performance and efficiency, making it accessible to users with limited computational resources while retaining the robust capabilities of the original Llama 2 model.

LoRA rank (r): 64
Alpha parameter: 16
Dropout probability: 0.1



- **Developed by:** lee12ki
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** 
- **Model type:** Causal Language Model (Instruction-tuned)
- **Language(s) (NLP):** English
- **License:** Llama2
- **Finetuned from model [optional]:** NousResearch/Llama-2-7b-chat-hf

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** lee12ki/llama2-finetune-7b
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --

This model can be used directly for generating text, answering questions, and conversational AI tasks.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

The model can be further fine-tuned for specific tasks such as customer support bots, code generation, or document summarization.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Avoid using the model for generating misinformation, hate speech, or other harmful content.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

This model may inherit biases from the training dataset or base model. Outputs should be reviewed critically before use in sensitive applications.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

Use the code below to get started with the model.

from transformers import pipeline

pipe = pipeline("text-generation", model="lee12ki/llama2-finetune-7b")
response = pipe("What is a large language model?")
print(response[0]["generated_text"])


## Training Details

### Training Data

<!-- 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. -->

The model was trained on mlabonne/guanaco-llama2-1k, a dataset tailored for instruction tuning, with dialogue-focused examples.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

The dataset was preprocessed to align with the LLaMA tokenizer format. Padding and sequence truncation were applied as required.


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

Training regime: fp16 mixed precision with gradient checkpointing
Batch size: 4 (per device)
Learning rate: 2e-4
Epochs: 1
Gradient Accumulation Steps: 1

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

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