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.model from kmeanskaran/gemma-code-instruct-finetune-test: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/kmeanskaran/gemma-code-instruct-finetune-test/resolve/main/tokenizer.model
- Command line
-
hf download hf://kmeanskaran/gemma-code-instruct-finetune-test/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/kmeanskaran/gemma-code-instruct-finetune-test/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
路
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