Instructions to use gasolsun/DynamicRAG-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gasolsun/DynamicRAG-8B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="gasolsun/DynamicRAG-8B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gasolsun/DynamicRAG-8B") model = AutoModelForCausalLM.from_pretrained("gasolsun/DynamicRAG-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from gasolsun/DynamicRAG-8B: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/gasolsun/DynamicRAG-8B/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://gasolsun/DynamicRAG-8B@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gasolsun/DynamicRAG-8B/resolve/refs%2Fpr%2F2/tokenizer.json
17.2 MB
- Xet hash:
- f50a225d9f53b6d10de736023ad24fa9ce92634801004eb39d881f415a4784cb
- Size of remote file:
- 17.2 MB
- SHA256:
- 3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
路
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