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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Sample Use
以下は、elyza-tasks-100-TV_0.jsonlの回答の為のコードです。
1from unsloth import FastLanguageModel
2from peft import PeftModel
3import torch
4import json
5from tqdm import tqdm
6import re
7
8
9HF_TOKEN = "{YOUR TOKEN}"
10
11!huggingface-cli login --token $HF_TOKEN
12!huggingface-cli download google/gemma-2-9b --local-dir gemma-2-9b/
13model_id = "./gemma-2-9b"
14adapter_id = "daidaidaidaidai/gemma-2-9b-sft_lora"
15
16dtype = None
17load_in_4bit = True
18
19model, tokenizer = FastLanguageModel.from_pretrained(
20 model_name=model_id,
21 dtype=dtype,
22 load_in_4bit=load_in_4bit,
23 trust_remote_code=True,
24)
25
26model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
27
28datasets = []
29with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
30 item = ""
31 for line in f:
32 line = line.strip()
33 item += line
34 if item.endswith("}"):
35 datasets.append(json.loads(item))
36 item = ""
37
38FastLanguageModel.for_inference(model)
39
40results = []
41for dt in tqdm(datasets):
42 input = dt["input"]
43
44 prompt = f"""### 指示\n{input}\n### 回答\n"""
45
46 inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
47
48 outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
49 prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
50
51 results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
52
