Llama3.1 Aloe Beta 8B
Are you looking for an AI model that can handle medical tasks with ease? Look no further than Llama3.1 Aloe Beta 8B. This open healthcare LLM is designed to excel in several different medical tasks, including text summarization, explanation, diagnosis, text classification, and treatment recommendation. With a robust and versatile architecture, Aloe Beta has been tested on popular healthcare QA datasets and has achieved state-of-the-art performance. But what really sets it apart is its ability to learn new capabilities like function calling, thanks to its diverse set of high-quality general-purpose data. Plus, its alignment and safety stages have been boosted to mitigate catastrophic forgetting and ensure safe use. Whether you're a researcher or a healthcare professional, Llama3.1 Aloe Beta 8B is a powerful tool that can help you achieve your goals.
Table of Contents
Model Overview
Aloe is a cutting-edge language model designed to revolutionize healthcare. Developed by HPAI, it’s a family of models that excel in various medical tasks, making it a game-changer in the healthcare industry.
Key Attributes
- Model Type: Causal decoder-only transformer language model
- Language: English (capable but not formally evaluated on other languages)
- License: Based on Meta Llama 3.1 8B, governed by the Meta Llama 3 License, with modifications available under CC BY 4.0 license
- Base Model: meta-llama/Llama-3.1-8B
Capabilities
Aloe is trained on 20 medical tasks, making it a robust and versatile healthcare model. It can:
- Summarize medical texts
- Explain medical concepts
- Diagnose medical conditions
- Classify medical texts
- Recommend treatments
- And more!
What sets Aloe apart?
Aloe outperforms many other medical models, including ==Llama3-OpenBioLLM== and ==Llama3-Med42==. When combined with a RAG system, Aloe’s performance is comparable to that of larger models like ==MedPalm-2== and GPT4.
Performance
Aloe achieves state-of-the-art performance on several medical tasks, outperforming other medical models. Its performance is significantly improved with prompting techniques, such as Medprompting, which provides a 7% increase in reported accuracy.
Evaluation Metrics
Aloe has been evaluated using a range of metrics, including accuracy, Rouge1, and more. The model has achieved high scores on these metrics, demonstrating its ability to accurately process and generate text.
Limitations
Aloe is not perfect, and it’s not intended for clinical practice or medical diagnosis. It can produce toxic content, and it’s prone to errors. Please use Aloe responsibly and under the supervision of a human expert.
Technical Limitations
Aloe has some technical limitations:
- Training data: While Aloe has been trained on a vast amount of data, it’s not exhaustive. There may be gaps in its knowledge or understanding of certain medical topics.
- Sequence length: Aloe has a limited sequence length, which can impact its ability to process complex or lengthy inputs.
- Optimization: Aloe’s optimization techniques may not always yield the best results, particularly in cases where the input is ambiguous or unclear.
Getting Started
You can get started with Aloe using the Transformers pipeline abstraction or the Auto classes with the generate() function. Here’s an example code snippet:
import transformers
import torch
model_id = "HPAI-BSC/Llama3.1-Aloe-Beta-8B"
pipeline = transformers.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
messages = [{"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."}, {"role": "user", "content": "Hello."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
terminators = [pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("")]
outputs = pipeline(prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9)
print(outputs[0]["generated_text"][len(prompt):])
