monsterapi/gemma-2-2b-norobots

Base Model for Fine-tuning: google/gemma-2-2b-it
Service Used: MonsterAPI
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:

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 or visit the official documentation.