File size: 2,266 Bytes
5b5d276 f8e0493 2f3694a 15865ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
license: creativeml-openrail-m
datasets:
- GEM/viggo
metrics:
- accuracy
library_name: transformers
tags:
- 'transformers '
- peft
- qlora
---
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id, # Mistral, same as before
quantization_config=bnb_config, # Same quantization config as before
device_map="auto",
trust_remote_code=True,
use_auth_token=True
)
eval_tokenizer = AutoTokenizer.from_pretrained(
base_model_id,
add_bos_token=True,
trust_remote_code=True,
)
```
Now load the QLoRA adapter from the appropriate checkpoint directory
```
from peft import PeftModel
ft_model = PeftModel.from_pretrained(base_model, "mistral-viggo-finetune/checkpoint-950")
```
Let's try the same eval_prompt and thus model_input as above, and see if the new finetuned model performs better.
```
eval_prompt = """Given a target sentence construct the underlying meaning representation of the input sentence as a single function with attributes and attribute values.
This function should describe the target string accurately and the function must be one of the following ['inform', 'request', 'give_opinion', 'confirm', 'verify_attribute', 'suggest', 'request_explanation', 'recommend', 'request_attribute'].
The attributes must be one of the following: ['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating', 'genres', 'player_perspective', 'has_multiplayer', 'platforms', 'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier']
### Target sentence:
Earlier, you stated that you didn't have strong feelings about PlayStation's Little Big Adventure. Is your opinion true for all games which don't have multiplayer?
### Meaning representation:
"""
model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
ft_model.eval()
with torch.no_grad():
print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
```
|