Question Answering
Transformers
Safetensors
English
qwen2
text-generation
biology
medical
healthcare
text-generation-inference
Instructions to use HPAI-BSC/Qwen2.5-Aloe-Beta-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPAI-BSC/Qwen2.5-Aloe-Beta-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="HPAI-BSC/Qwen2.5-Aloe-Beta-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HPAI-BSC/Qwen2.5-Aloe-Beta-7B") model = AutoModelForCausalLM.from_pretrained("HPAI-BSC/Qwen2.5-Aloe-Beta-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -38,7 +38,7 @@ Aloe: A Family of Fine-tuned Open Healthcare LLMs
|
|
| 38 |
|
| 39 |
Qwen2.5-Aloe-Beta-7B is an **open healthcare LLM** achieving **state-of-the-art performance** on several medical tasks. Aloe Beta is made available in four model sizes: [7B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-7B/), [8B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-8B), [70B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-70B), and [72B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-72B). All models are trained using the same recipe, on top of two different families of models: Llama3.1 and Qwen2.5.
|
| 40 |
|
| 41 |
-
Aloe is trained on 20 medical tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system ([also released](https://github.com/HPAI-BSC/prompt_engine)) the 8B version gets close to the performance of closed models like MedPalm-2, GPT4. With the same RAG system, Aloe-Beta-70B outperforms those private alternatives, producing state-of-the-art results.
|
| 42 |
|
| 43 |
# Aloe-Beta-7B
|
| 44 |
|
|
|
|
| 38 |
|
| 39 |
Qwen2.5-Aloe-Beta-7B is an **open healthcare LLM** achieving **state-of-the-art performance** on several medical tasks. Aloe Beta is made available in four model sizes: [7B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-7B/), [8B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-8B), [70B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-70B), and [72B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-72B). All models are trained using the same recipe, on top of two different families of models: Llama3.1 and Qwen2.5.
|
| 40 |
|
| 41 |
+
Aloe is trained on 20 medical tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system ([also released](https://github.com/HPAI-BSC/prompt_engine)) the 7B and 8B version gets close to the performance of closed models like MedPalm-2, GPT4. With the same RAG system, Llama3.1-Aloe-Beta-70B and Qwen2.5-Aloe-Beta-72B outperforms those private alternatives, producing state-of-the-art results.
|
| 42 |
|
| 43 |
# Aloe-Beta-7B
|
| 44 |
|