Instructions to use Mehdi-Zogh/MNLP_M2_dpo_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mehdi-Zogh/MNLP_M2_dpo_model 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="Mehdi-Zogh/MNLP_M2_dpo_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Mehdi-Zogh/MNLP_M2_dpo_model") model = AutoModelForCausalLM.from_pretrained("Mehdi-Zogh/MNLP_M2_dpo_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Mehdi-Zogh/MNLP_M2_dpo_model: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Mehdi-Zogh/MNLP_M2_dpo_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Mehdi-Zogh/MNLP_M2_dpo_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Mehdi-Zogh/MNLP_M2_dpo_model/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 6aec39639a0a2d1ca966356b8c2b8426a484f80ff80731f44fa8482040713bdf
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
路
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