LLM-JP-3.3.7B LoRA Model
This is the LLM-JP-3.3.7B model fine-tuned with LoRA for instruction-based Japanese text generation tasks. The model has been fine-tuned on datasets ichikara-instruction and Ego/jpflan-raw.
How to Use
Below is an example of how to use the model for inference:
import torch
from unsloth import FastLanguageModel
from peft import PeftModel
HF_TOKEN = "" # Add your Hugging Face token here
# Load the base model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="llm-jp/llm-jp-3-3.7b",
dtype=None,
load_in_4bit=True,
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, "nito78/llm-jp-3-3.7b-it_lora_all", token=HF_TOKEN)
# Switch to inference mode
FastLanguageModel.for_inference(model)
# Example usage
import json
# Load dataset
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 = ""
from tqdm import tqdm
# Perform inference
results = []
for dt in tqdm(datasets):
input = dt["input"]
prompt = f"""### 指示\n{input}\n### 回ç”\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ç”")[-1]
results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
import os
import json
# Save results
output_dir = "./results"
os.makedirs(output_dir, exist_ok=True)
output_file = os.path.join(output_dir, "result.jsonl")
with open(output_file, "w", encoding="utf-8") as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write("\n")
Model tree for nito78/llm-jp-3-3.7b-it_lora_all
Base model
llm-jp/llm-jp-3-3.7b