Question Answering
Transformers
Safetensors
English
German
llama
text-generation
German
RAG
Retrieval
Question-Answering
Summarization
Reasoning
text-generation-inference
Instructions to use avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI") model = AutoModelForCausalLM.from_pretrained("avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a26fc1cef9aabda9cc8b7d3e0b2c8877ce4a8b003f5ac9186f2d7ccdad6029fb
- Size of remote file:
- 2.67 GB
- SHA256:
- ef268b43837c2845a8ce391c6822444bfb2191e52d0cd50f64e0d7aacfd7fdf1
路
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