Instructions to use komatsurui/llm-jp-3-13b-it_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use komatsurui/llm-jp-3-13b-it_lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("komatsurui/llm-jp-3-13b-it_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use komatsurui/llm-jp-3-13b-it_lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for komatsurui/llm-jp-3-13b-it_lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for komatsurui/llm-jp-3-13b-it_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for komatsurui/llm-jp-3-13b-it_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="komatsurui/llm-jp-3-13b-it_lora", max_seq_length=2048, )
Uploaded model
- Developed by: komatsurui
- 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.
データセットの読み込み
"""python
import json datasets = [] with open("/content/drive/MyDrive/LLM_final_competition/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 = ""
推論実行
"""python
from tqdm import tqdm
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})
結果の保存
"""python
with open(f"{new_model_id}_output.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) f.write('\n')
Model tree for komatsurui/llm-jp-3-13b-it_lora
Base model
llm-jp/llm-jp-3-13b