Instructions to use AdityaPandey/mistral_128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AdityaPandey/mistral_128k with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Yarn-Mistral-7B-128k-GPTQ") model = PeftModel.from_pretrained(base_model, "AdityaPandey/mistral_128k") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AdityaPandey/mistral_128k: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/AdityaPandey/mistral_128k/resolve/1dab8b45f9d23748ca09462fb79f8a02f2b5c97b/training_args.bin
- Command line
-
hf download hf://AdityaPandey/mistral_128k@1dab8b45f9d23748ca09462fb79f8a02f2b5c97b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AdityaPandey/mistral_128k/resolve/1dab8b45f9d23748ca09462fb79f8a02f2b5c97b/training_args.bin
4.73 kB
- Xet hash:
- d1d5cf4d94f79dbf2102747dbffac7b31a324bc566035c69360510eb1d6f93e6
- Size of remote file:
- 4.73 kB
- SHA256:
- ad951dfecddbabab67d8ccf76a4403378cce04c3f644774f5b3407fb6d77641c
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