gemma-2-9b-sft_lora

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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。

from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re


HF_TOKEN = "{YOUR TOKEN}"

!huggingface-cli login --token $HF_TOKEN
!huggingface-cli download google/gemma-2-9b --local-dir gemma-2-9b/
model_id = "./gemma-2-9b"
adapter_id = "daidaidaidaidai/gemma-2-9b-sft_lora"

dtype = None 
load_in_4bit = True 

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_id,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)

datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
    item = ""
    for line in f:
      line = line.strip()
      item += line
      if item.endswith("}"):
        datasets.append(json.loads(item))
        item = ""

FastLanguageModel.for_inference(model)

results = []
for dt in tqdm(datasets):
  input = dt["input"]

  prompt = f"""### 指示\n{input}\n### 回答\n"""

  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)

  outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
  prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})