Instructions to use feedback-to-code/swe-diff-llama-3-8B-Instruct-First with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use feedback-to-code/swe-diff-llama-3-8B-Instruct-First with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "feedback-to-code/swe-diff-llama-3-8B-Instruct-First") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from feedback-to-code/swe-diff-llama-3-8B-Instruct-First: direct link, hf CLI and curl.
- Browser
- Download file 9.09 MB
-
https://huggingface.co/feedback-to-code/swe-diff-llama-3-8B-Instruct-First/resolve/main/tokenizer.json
- Command line
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hf download hf://feedback-to-code/swe-diff-llama-3-8B-Instruct-First/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/feedback-to-code/swe-diff-llama-3-8B-Instruct-First/resolve/main/tokenizer.json
9.09 MB
File too large to display, you can check the raw version instead.