Instructions to use kmeanskaran/gemma-code-instruct-finetune-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmeanskaran/gemma-code-instruct-finetune-test 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="kmeanskaran/gemma-code-instruct-finetune-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kmeanskaran/gemma-code-instruct-finetune-test") model = AutoModelForCausalLM.from_pretrained("kmeanskaran/gemma-code-instruct-finetune-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kmeanskaran/gemma-code-instruct-finetune-test: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/kmeanskaran/gemma-code-instruct-finetune-test/resolve/main/tokenizer.json
- Command line
-
hf download hf://kmeanskaran/gemma-code-instruct-finetune-test/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kmeanskaran/gemma-code-instruct-finetune-test/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- b2611901e7f530fff676b1ca1e80565c6fcd5d8268cc9b29dd32ad481f31e1df
- Size of remote file:
- 17.5 MB
- SHA256:
- f30f819ff5b0f4cef2c8a6aafbeb20a13e7dd14409ece0f1e11e1d84bcfd281b
路
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