Instructions to use BiniyamAjaw/llama-2-7b-finetuned-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BiniyamAjaw/llama-2-7b-finetuned-adapters with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "BiniyamAjaw/llama-2-7b-finetuned-adapters") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **License:** [MIT]
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- **Finetuned from model [optional]:** [NousResearch-Llama2-7B-hf]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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The model is still in development and significantly lacks training data so it might not generate contents the way you want it to.
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You can fine tune this model on labeled data for a specific domain. To get more pleasing results.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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- **License:** [MIT]
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- **Finetuned from model [optional]:** [NousResearch-Llama2-7B-hf]
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## Uses
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The model is still in development and significantly lacks training data so it might not generate contents the way you want it to.
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You can fine tune this model on labeled data for a specific domain. To get more pleasing results.
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## Bias, Risks, and Limitations
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