qcube/llm-jp-3-13b-finetune6

Uploaded model

• Developed by: qcube
• License: apache-2.0
• Finetuned from model : llm-jp/llm-jp-3-13b

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Sample use

以下は、elyza-tasks-100-TV_0.jsonl の回答のためのコードです。

from unsloth import FastLanguageModel
import torch
import json


HF_TOKEN = "your-token"
model_name = "qcube/llm-jp-3-13b-finetune6"

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    token=HF_TOKEN,
)
FastLanguageModel.for_inference(model)

# データセットの読み込み。
# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。
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

# 推論
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})


with open(f"./llm-jp-3-13b-finetune6-outputs-3.jsonl", "w", encoding="utf-8") as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write("\n")