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---
library_name: transformers
license: mit
language:
- ja
base_model:
- llm-jp/llm-jp-3-13b
---
## Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Hiroaki Hara(@Himalayan-wildcat)
- **Language(s) (NLP):** ja
- **License:** MIT
- **Finetuned from model [optional]:** llm-jp/llm-jp-3-13b
## Uses
```Python
import json
import re
import torch
from peft import PeftModel
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
model_id = "hiroakihara/llm-jp-3-13b-finetune"
hf_token = "YOUR HUGGINGFACE TOKEN"
test_jsonl_data = "elyza-tasks-100-TV_0.jsonl"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto",
token = hf_token)
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True,
token=hf_token)
datasets = []
with open(test_jsonl_data) as f:
item = ""
for line in f:
line = line.strip()
item += line
if item.endswith("}"):
datasets.append(json.loads(item))
item = ""
results = []
for data in tqdm(datasets):
input = data["input"]
prompt = f"""### 指示
{input}
### 回答
"""
tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(tokenized_input)
with torch.no_grad():
outputs = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=100,
do_sample=False,
repetition_penalty=1.2,
pad_token_id=tokenizer.eos_token_id
)[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
results.append({"task_id": data["task_id"], "input": input, "output": output})
jsonl_id = re.sub(".*/", "", model_id)
with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
``` |