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 checkpoints/checkpoint-2/optimizer.pt from feedback-to-code/swe-diff-llama-3-8B-Instruct-First: direct link, hf CLI and curl.
- Browser
- Download file 84.6 MB
-
https://huggingface.co/feedback-to-code/swe-diff-llama-3-8B-Instruct-First/resolve/main/checkpoints/checkpoint-2/optimizer.pt
- Command line
-
hf download hf://feedback-to-code/swe-diff-llama-3-8B-Instruct-First/checkpoints/checkpoint-2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/feedback-to-code/swe-diff-llama-3-8B-Instruct-First/resolve/main/checkpoints/checkpoint-2/optimizer.pt
84.6 MB
- Xet hash:
- a3efa17bf8e898d5b88f7beaab895c55cd4a377c560f657c710ad6e02fe6daf7
- Size of remote file:
- 84.6 MB
- SHA256:
- d9d80e9f0939ee5a0c92005e7f11894422aeb3400605cec02149da2a59013ff3
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