Instructions to use saishshinde15/Reasoning_model_llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saishshinde15/Reasoning_model_llama 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="saishshinde15/Reasoning_model_llama")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("saishshinde15/Reasoning_model_llama", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from saishshinde15/Reasoning_model_llama: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/saishshinde15/Reasoning_model_llama/resolve/main/tokenizer.json
- Command line
-
hf download hf://saishshinde15/Reasoning_model_llama/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/saishshinde15/Reasoning_model_llama/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- da070f8626ee36c29d81d14f7a18b0a943ba6477aaa86b433d0f865f98bf8392
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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