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--- |
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library_name: peft |
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base_model: meta-llama/Llama-2-7b-hf |
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license: llama2 |
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datasets: |
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- freQuensy23/toxic-answers |
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language: |
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- en |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by: t.me/freQuensy23 |
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- **Model type: LLM (Llama2) |
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- **Language(s) (NLP): EN |
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- **License:** Llama-2-license |
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- **Finetuned from model [optional]: meta-llama/Llama-2-7b |
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### Model Sources [optional] |
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## How to Get Started with the Model |
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```python |
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import peft |
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import transformers |
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model = peft.AutoPeftModelForCausalLM.from_pretrained('freQuensy23/toxic-llama2') |
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tokenizer = transformers.AutoTokenizer.from_pretrained('transformers') |
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print(tokenizer.batch_decode(input_ids=tokenizer('User: What is 1 + 8?\nBot:', return_tensors='pt').input_ids)) |
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``` |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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https://huggingface.co/freQuensy23/toxic-llama2 |
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[More Information Needed] |
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### Results |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** A-100 |
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- **Hours used:** 1 |
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- **Cloud Provider:** Yandex-cloud |
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- **Compute Region:** Moscow |
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- **Carbon Emitted:** 11g |
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## Model Card Contact |
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t.me/freQuensy23 |
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github.com/freQuensy23-coder |
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[email protected] |
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### Framework versions |
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- PEFT 0.7.1 |
