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--- |
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library_name: peft |
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base_model: mistralai/Mistral-7B-Instruct-v0.2 |
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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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This model is a fine-tuned version of [base_model](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an [FRIENDS TV Series](https://www.kaggle.com/datasets/blessondensil294/friends-tv-series-screenplay-script) dataset. |
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Fine-tuning was done by taking only the parts of the dataset where Monica spoke. |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel, PeftConfig |
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base_model = "mistralai/Mistral-7B-Instruct-v0.2" |
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adapter_model = "akingunduz/monica_llm" |
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model = AutoModelForCausalLM.from_pretrained(base_model) |
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model = PeftModel.from_pretrained(model, adapter_model) |
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
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model = model.to("cuda") |
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model.eval() |
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import torch |
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def build_prompt(question): |
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prompt=f"<s>[INST]@Monica. {question} [/INST]" |
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return prompt |
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question = "Which city do you live?" |
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prompt = build_prompt(question) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=10) |
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print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]) |
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``` |
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``` |
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>>> [INST]@Monica. Which city do you live? [/INST]New York. |
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``` |
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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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- PEFT 0.10.0 |
