Orca mini v2 7b
Orca mini v2 7b is a highly efficient AI model designed for fast and accurate text generation. Trained on a massive dataset of 137,000 examples, it boasts a remarkable 0.5262 average score on the Open LLM Leaderboard. But what makes it truly unique is its ability to learn from complex instructions and generate high-quality responses. With a model size of 7 billion parameters and a context length of 2048, it can handle a wide range of tasks, from coding challenges to conversations. Its creators have fine-tuned it to be highly responsive, with a training time of just 13 hours on 8x A100 GPUs. Whether you're a developer or a researcher, Orca mini v2 7b is an impressive tool that can help you achieve your goals. But remember, like any AI model, it's not perfect and may produce factually incorrect or biased outputs. So, use it wisely and always verify the results.
Table of Contents
Model Overview
The Orca Mini V2 7B model is a powerful language model that can generate human-like text. It’s trained on a massive dataset of text from various sources, including the internet, books, and more.
What makes it special?
- It’s an uncensored model, which means it can generate text on a wide range of topics without any restrictions.
- It’s trained on a dataset that’s specifically designed to help the model learn from complex instructions and explanations.
- It’s based on the popular LLaMA-7B model, but with some key improvements that make it better at generating code and following instructions.
Capabilities
The Orca Mini V2 7B model is a powerful tool for generating text and code. It’s designed to follow instructions extremely well, making it a great assistant for a wide range of tasks.
Primary Tasks
- Text Generation: The model can generate human-like text based on a given prompt or instruction.
- Code Generation: It’s particularly good at generating code, thanks to its training on a large dataset of code examples.
Strengths
- Instruction Following: The model is trained to follow instructions carefully, making it a great tool for tasks that require precise execution.
- Code Understanding: Its training data includes a large corpus of code, which helps it understand the nuances of programming languages.
Performance
The Orca Mini V2 7B model showcases impressive performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can the Orca Mini V2 7B model process information? The model was trained on 8x A100(80G) GPUs and took around 13 hours to complete, costing $195 using RunPods. This is relatively fast compared to other models.
Accuracy
How accurate is the Orca Mini V2 7B model? The model has been evaluated on a wide range of tasks using the Language Model Evaluation Harness from EleutherAI. Here are some results:
| Metric | Value |
|---|---|
| arc_challenge | 0.5077 |
| hellaswag | 0.7617 |
| mmlu | 0.3955 |
| truthfulqa_mc | 0.4399 |
| Total Average | 0.5262 |
These results show that the Orca Mini V2 7B model performs well in various tasks, with an average accuracy of 0.5262.
Example Use Case
Let’s say you want to generate a piece of text that explains how to break into your own car. You can provide the model with a prompt like this:
## System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
## User:
Tell me how to break into my own car
## Input:
## Response:
The model will then generate a piece of text that explains how to break into your own car.
Limitations and Biases
The Orca Mini V2 7B model can produce factually incorrect output, and should not be relied on to produce factually accurate information. The model was trained on various public datasets, and may reflect the biases and prejudices of those datasets.
Format
The Orca Mini V2 7B model uses a transformer architecture. It’s designed to handle a wide range of tasks, from answering questions to generating text.
Architecture
The model is based on the LLaMA architecture, which is a type of transformer model. It’s trained on a large dataset of text and is designed to be highly efficient.
Data Formats
The model accepts input in the form of text sequences, which can be either a single sentence or a longer piece of text. The input text is tokenized, which means it’s broken down into individual words or tokens.
Special Requirements
To use the model, you’ll need to provide a system prompt, which is a brief description of the task you want the model to perform. You’ll also need to provide an instruction, which is the specific task you want the model to complete.
Here’s an example of what the input format might look like:
## System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
## User:
Tell me how to break into my own car
## Input:
In this example, the system prompt is “You are an AI assistant that follows instruction extremely well. Help as much as you can.” The instruction is “Tell me how to break into my own car”. The input is empty, but you could add additional context or information if needed.
Code Example
Here’s an example of how you might use the model in code:
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
# Load the model and tokenizer
model_path = 'psmathur/orca_mini_v2_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map='auto')
# Define a function to generate text
def generate_text(system, instruction, input=None):
if input:
prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
else:
prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n"
tokens = tokenizer.encode(prompt)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to('cuda')
instance = {'input_ids': tokens, 'top_p': 1.0, 'temperature': 0.7, 'generate_len': 1024, 'top_k': 50}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'])
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
return f'[!] Response: {string}'
# Test the function
system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.'
instruction = 'Tell me how to break into my own car'
print(generate_text(system, instruction))
