Instructions to use wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Python-hf") model = PeftModel.from_pretrained(base_model, "wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/resolve/main/adapter_model.bin
- Command line
-
hf download hf://wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/wesley7137/MetaMath-CodeLlama-Python-7B-V1-lora/resolve/main/adapter_model.bin
134 MB
- Xet hash:
- e4c8d1e447c50dd5d96df45745b3e81d280f3ce9dd7dc4a8af813c887362bb31
- Size of remote file:
- 134 MB
- SHA256:
- 735bb6d68d99613e202c4b05d8c26301901cc146bf950b9c29bb4342f22de131
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