---
language: en
datasets:
  - fka/awesome-chatgpt-prompts
tags:
  - llama-2
  - fine-tuning
  - causal-lm
  - chatgpt
license: apache-2.0
model_name: fine-tuned-llama2
widget:
  - text: "I want you to act as a Linux terminal."
  - text: "Imagine you are an Ethereum Developer tasked with creating a smart contract."
---

# Fine-Tuned Llama 2 Model

## Model Description

This model is a fine-tuned version of Llama 2, trained on a dataset of diverse prompts and scenarios. The model has been designed to generate responses based on various tasks described in the `prompt` column of the dataset. The fine-tuning process aims to improve the model's performance in handling specific tasks across multiple domains, such as software development, SEO, and Linux commands.

## Dataset Information

The dataset used for fine-tuning this model consists of two primary columns:

1. **`act`**: The role or scenario that the model is asked to act upon. For example:
   - "An Ethereum Developer"
   - "SEO Prompt"
   - "Linux Terminal"

2. **`prompt`**: The detailed task or scenario description related to the `act`. This provides context and specific instructions that the model needs to follow. Example prompts:
   - *"Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain messenger..."*
   - *"Using WebPilot, create an outline for an article that will be 2,000 words on the keyword 'Best SEO prompts'..."*
   - *"I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show..."*

The dataset includes a wide range of scenarios aimed at helping the model generalize across technical and creative tasks.

### Dataset Samples

| **Act**            | **Prompt**                                                                                                   |
|--------------------|-------------------------------------------------------------------------------------------------------------|
| Ethereum Developer  | Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain...  |
| SEO Prompt          | Using WebPilot, create an outline for an article that will be 2,000 words on the keyword 'Best SEO prompts'...|
| Linux Terminal      | I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show...|

## Output Example

The model has been fine-tuned to generate detailed, contextually relevant responses based on the prompts provided. Here’s an example of how the model might respond to a sample prompt:

### Input:
**Act**: *Linux Terminal*

### Output:
**Prompt**: 
*I want you to act as a Linux terminal. I will type commands, and you will reply with what the terminal should show. Execute the command `ls`.*

In this scenario, the model understands that it should act as a Linux terminal and simulate the result of running the `ls` command.

### Another Example:

### Input:
**Act**: *Ethereum Developer*

### Output:
**Prompt**:
*Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain messenger...*

In this example, the model generates Solidity code based on the prompt, addressing the requirements for a blockchain messenger.

## How to Use the Model

This model can be loaded and used through the Hugging Face Hub:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Manish-KT/Fine_tune_Llama_2")
model = AutoModelForCausalLM.from_pretrained("Manish-KT/Fine_tune_Llama_2")

# Encode the prompt
inputs = tokenizer("I want you to act as a linux terminal.", return_tensors="pt")

# Generate the response
outputs = model.generate(inputs["input_ids"], max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Acknowledgements

Special thanks to the creators of the dataset `fka/awesome-chatgpt-prompts`, which provided the rich prompts and diverse scenarios used in fine-tuning this model.

## License
This model is open-sourced and can be used for both commercial and non-commercial purposes. Please ensure that you attribute the original dataset and respect any usage policies.
---
license: mit
---