Instructions to use klcsp/gemma7b-lora-coding-11-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use klcsp/gemma7b-lora-coding-11-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") model = PeftModel.from_pretrained(base_model, "klcsp/gemma7b-lora-coding-11-v1") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from klcsp/gemma7b-lora-coding-11-v1: direct link, hf CLI and curl.
- Browser
- Download file 34.3 MB
-
https://huggingface.co/klcsp/gemma7b-lora-coding-11-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://klcsp/gemma7b-lora-coding-11-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/klcsp/gemma7b-lora-coding-11-v1/resolve/main/tokenizer.json
34.3 MB
- Xet hash:
- 4fff413e42d14c8fa35c4143f9535bfa1ccb8a2b573da2e6537d4c1a4151873b
- Size of remote file:
- 34.3 MB
- SHA256:
- c644094fa37d155fce4eccae9d5a12bb1723fe4439b0b6a2ebbe173755b2c218
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