README.md

---
library_name: peft
tags:
- nlp
- code
- instruct
- llama
datasets:
- HuggingFaceH4/no_robots
base_model: google/gemma-2-2b-it
license: apache-2.0
---


# monsterapi/gemma-2-2b-norobots

**Base Model for Fine-tuning:** [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)  
**Service Used:** [MonsterAPI](https://monsterapi.ai)  
**License:** Apache-2.0  

## Overview

`monsterapi/gemma-2-2b-norobots` is a fine-tuned language model designed to improve instruction-following capabilities. The model was trained using the "No Robots" dataset, a high-quality set of 10,000 instructions and demonstrations curated by expert human annotators. This fine-tuning process enhances the base model's performance in understanding and executing single-turn instructions, similar to the goals outlined in OpenAI's InstructGPT.

### Dataset Details

**Dataset Summary:**  
The "No Robots" dataset is a collection of 10,000 high-quality instructions and demonstrations created by skilled human annotators. The dataset is modeled after the instruction dataset described in OpenAI's InstructGPT paper. It mainly includes single-turn instructions across various categories, aiming to improve the instruction-following capabilities of language models during supervised fine-tuning (SFT).

## Fine-tuning Details

**Fine-tuned Model Name:** `monsterapi/gemma-2-2b-norobots`  
**Training Time:** 31 minutes  
**Cost:** $1.10  
**Epochs:** 1  
**Gradient Accumulation Steps:** 32  

The model was fine-tuned using MonsterAPI's finetuning service, optimizing the base model `google/gemma-2-2b-it` to perform better on instruction-following tasks. 

## Hyperparameters & Additional Details

- **Base Model:** `google/gemma-2-2b-it`
- **Dataset:** No Robots (10,000 instructions and demonstrations)
- **Training Duration:** 31 minutes
- **Cost per Epoch:** $1.10
- **Total Finetuning Cost:** $1.10
- **Gradient Accumulation Steps:** 32

## Use Cases

This model is well-suited for tasks that require improved instruction-following capabilities, such as:
- Chatbots and virtual assistants
- Content creation tools
- Automated customer support systems
- Task automation in various industries

## How to Use

You can load the model directly using the Hugging Face Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "monsterapi/gemma-2-2b-norobots"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
input_text = "Explain the concept of supervised fine-tuning in simple terms."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Acknowledgements

The fine-tuning process was carried out using MonsterAPI's finetuning service, which offers a seamless experience for optimizing large language models.

## Contact

For further details or queries, please contact [MonsterAPI](https://monsterapi.ai) or visit the official documentation.