Instructions to use DS-Archive/limarp-miqu-1-70b-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DS-Archive/limarp-miqu-1-70b-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("models/miqu-1-70b-sf") model = PeftModel.from_pretrained(base_model, "DS-Archive/limarp-miqu-1-70b-qlora") - Notebooks
- Google Colab
- Kaggle
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# limarp-miqu-1-70b-qlora
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Experimental limarp qlora trained at 16384 ctx length (greater than size of the longest limarp sample when tokenized via
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I wasn't particularly happy with the results I got when I tried applying the lora at varying weights to the miqu-1-70b model. It's possible that this is related to the fact that the model was dequantized from Q5_K_M GGUF, or perhaps due to it already being an instruct-tuned model.
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However, I decided to go ahead and release this in case someone else finds a use for it.
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## Model description
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# limarp-miqu-1-70b-qlora
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Experimental limarp qlora trained at 16384 ctx length (greater than size of the longest limarp sample when tokenized via llama's tokenizer) on the fixed dequantized miqu-1-70b model by 152334H.
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I wasn't particularly happy with the results I got when I tried applying the lora at varying weights to the miqu-1-70b model. It's possible that this is related to the fact that the model was dequantized from Q5_K_M GGUF, or perhaps due to it already being an instruct-tuned model.
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However, I decided to go ahead and release this in case someone else finds a use for it. Provided as-is and YMMV.
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## Model description
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