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AI-Harness evaluation; link
| Model | Copa | HellaSwag | BoolQ | MMLU |
|---|---|---|---|---|
| 0-shot | 0-shot | 0-shot | 0-shot | |
| phi-2-platypus-Commercial-lora | 0.8900 | 0.5573 | 0.8260 | 0.5513 |
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4torch.set_default_device("cuda")
5
6model = AutoModelForCausalLM.from_pretrained("field2437/phi-2-platypus-Commercial-lora", torch_dtype="auto", trust_remote_code=True)
7tokenizer = AutoTokenizer.from_pretrained("field2437/phi-2-platypus-Commercial-lora", trust_remote_code=True)
8
9inputs = tokenizer('''Below is an instruction that describes a task. Write a response that appropriately completes the request.
10
11### Instruction:
12Let $f(x)$ be the polynomial \\[f(x)=3x^4+5x^2-9x-2.\\] If $g(x)$ is equal to the polynomial $f(x-1)$, what is the sum of the coefficients of $g$?
13
14### Response:
15''', return_tensors="pt", return_attention_mask=False)
16
17outputs = model.generate(**inputs, max_length=200)
18text = tokenizer.batch_decode(outputs)[0]
19print(text)