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--- |
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language: en |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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tags: |
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- llama-2 |
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- fine-tuning |
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- causal-lm |
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- chatgpt |
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license: apache-2.0 |
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model_name: fine-tuned-llama2 |
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widget: |
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- text: "I want you to act as a Linux terminal." |
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- text: "Imagine you are an Ethereum Developer tasked with creating a smart contract." |
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--- |
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# Fine-Tuned Llama 2 Model |
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## Model Description |
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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. |
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## Dataset Information |
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The dataset used for fine-tuning this model consists of two primary columns: |
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1. **`act`**: The role or scenario that the model is asked to act upon. For example: |
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- "An Ethereum Developer" |
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- "SEO Prompt" |
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- "Linux Terminal" |
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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: |
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- *"Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain messenger..."* |
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- *"Using WebPilot, create an outline for an article that will be 2,000 words on the keyword 'Best SEO prompts'..."* |
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- *"I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show..."* |
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The dataset includes a wide range of scenarios aimed at helping the model generalize across technical and creative tasks. |
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### Dataset Samples |
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| **Act** | **Prompt** | |
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|--------------------|-------------------------------------------------------------------------------------------------------------| |
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| Ethereum Developer | Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain... | |
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| SEO Prompt | Using WebPilot, create an outline for an article that will be 2,000 words on the keyword 'Best SEO prompts'...| |
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| 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...| |
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## Output Example |
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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: |
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### Input: |
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**Act**: *Linux Terminal* |
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### Output: |
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**Prompt**: |
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*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`.* |
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In this scenario, the model understands that it should act as a Linux terminal and simulate the result of running the `ls` command. |
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### Another Example: |
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### Input: |
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**Act**: *Ethereum Developer* |
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### Output: |
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**Prompt**: |
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*Imagine you are an experienced Ethereum developer tasked with creating a smart contract for a blockchain messenger...* |
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In this example, the model generates Solidity code based on the prompt, addressing the requirements for a blockchain messenger. |
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## How to Use the Model |
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This model can be loaded and used through the Hugging Face Hub: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Manish-KT/Fine_tune_Llama_2") |
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model = AutoModelForCausalLM.from_pretrained("Manish-KT/Fine_tune_Llama_2") |
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# Encode the prompt |
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inputs = tokenizer("I want you to act as a linux terminal.", return_tensors="pt") |
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# Generate the response |
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outputs = model.generate(inputs["input_ids"], max_length=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Acknowledgements |
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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. |
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## License |
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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. |
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--- |
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license: mit |
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--- |
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