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
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README.md
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We also compared the performance of the model in the general domain, using the OpenLLM Leaderboard benchmark. Aloe-Beta gets competitive results with the current SOTA general models in the most used general benchmarks and outperforms the medical models:
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We also compared the performance of the model in the general domain, using the OpenLLM Leaderboard benchmark. Aloe-Beta gets competitive results with the current SOTA general models in the most used general benchmarks and outperforms the medical models:
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