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--- |
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license: apache-2.0 |
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--- |
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How to use: |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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# Load Base Model |
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base_model_id = "mistralai/Mistral-7B-v0.1" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config) |
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eval_tokenizer = AutoTokenizer.from_pretrained( |
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base_model_id, |
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add_bos_token=True, |
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trust_remote_code=True, |
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) |
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eval_tokenizer.pad_token = eval_tokenizer.eos_token |
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# Load Peft Weights |
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from peft import PeftModel |
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ft_model = PeftModel.from_pretrained(model, "mistral-samsum-finetune/checkpoint-150") |
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# Format the Sample Input |
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def formatting_func(example): |
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text = f"### Summarize this dialog:\n{example['dialogue']}\n### Summary:\n{example['summary']}" |
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return text |
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max_length = 256 |
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eval_prompt = {'dialogue': "Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-)", |
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'summary': ''} |
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eval_prompt = formatting_func(eval_prompt) |
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# Generate summary for sample Input |
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model_input = eval_tokenizer( |
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eval_prompt, |
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truncation=True, |
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max_length=max_length, |
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padding="max_length", |
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return_tensors="pt").to("cuda") |
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ft_model.eval() |
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with torch.no_grad(): |
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print(eval_tokenizer.decode(ft_model.generate(**model_input, |
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max_new_tokens=256, |
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repetition_penalty=1.15)[0], |
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skip_special_tokens=True)) |
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# here is the output: |
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""" |
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### Summarize this dialog: |
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Amanda: I baked cookies. Do you want some? Jerry: Sure! Amanda: I'll bring you tomorrow :-) |
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### Summary: |
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Jerry will get some cookies from Amanda tomorrow. |
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""" |
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``` |
