This model is an instruction-tuned variant of llm-jp/llm-jp-3-13b-instruct, further fine-tuned on a subset of nvidia/OpenMathInstruct-2 with 256,000 samples. The fine-tuning process followed a parameter-efficient strategy, updating only selected layers while freezing most of the model parameters.

Key Features

Base Model: llm-jp/llm-jp-3-13b-instruct
Fine-Tuning Data: 256,000 samples from nvidia/OpenMathInstruct-2
Updated Parameters:
- All parameters were frozen except for:
  python
  1for param in model.parameters(): 2 param.requires_grad = False 3 4for param in model.lm_head.parameters(): 5 param.requires_grad = True
Macro-o1 Tokens Added: To align with Marco-o1, we introduced the following special tokens:
- <Thought>, </Thought>
- <Output>, </Output>
Reasoning Model Integration: Uses the implementation from Hajime-Y/reasoning-model

Usage

Below is an example of using the model with Monte Carlo Tree Search (MCTS) for reasoning:

python

import sys
import torch
sys.path.append('./reasoning-model')  
from reasoning_model import ReasoningModelForCausalLM
from tree_utils import print_tree_with_best_path
from transformers import AutoTokenizer

# tokenizerとmodelの準備
model_name = "doshisha-mil/llm-jp-13b-OpenMathInstruct-2-v1.1"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True,
)

# パディングトークンを明示的に設定
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# モデルのロード
model = ReasoningModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# 入力テキスト
prompt = "Find the number of positive integers $x$ that satisfy $x^{-1}>x$."
text = f"あなたは優秀で論理的なアシスタントです。まずは<Thought></Thought>タグの中であなたの思考の過程を記載し、<Output></Output>タグの中に最終的にユーザーに提供する出力を記載します。\n\n### 指示: {prompt}\n\n### 応答: <Thought>\n"

# Tokenize with explicit attention_mask
model_inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True)
model_inputs["attention_mask"] = (model_inputs["input_ids"] != tokenizer.pad_token_id).long()

# デバイスをモデルのデバイスに統一
model_inputs = {key: val.to(model.device) for key, val in model_inputs.items()}

# MCTSを用いて生成
final_tokens, final_node = model.generate(
    input_ids=model_inputs["input_ids"],
    attention_mask=model_inputs["attention_mask"],  # 明示的に attention_mask を渡す
    iterations_per_step=3,
    max_iterations=30,
    mini_step_size=32,
    expand_threshold=0,
    step_separator_ids=None,
)

# 結果をテキスト化
final_text = tokenizer.decode(final_tokens, skip_special_tokens=True)
print("=== 最終生成テキスト ===")
print(final_text)

Model Applications

Mathematical problem-solving with structured reasoning
Chain-of-Thought (CoT) enhanced reasoning
Integration with Monte Carlo Tree Search (MCTS)
Instruction-based question answering

References

Base Model: llm-jp/llm-jp-3-13b-instruct
Dataset: nvidia/OpenMathInstruct-2
Marco-o1 Paper: arXiv:2411.14405v1
Reasoning Model Code: Hajime-Y/reasoning-model

Citation

If you use this model, please cite the original base model and relevant datasets.

bibtex

@article{llm-jp3-13b-instruct,
  title={LLM-JP 3-13B Instruct},
  author={LLM-JP Team},
  year={2024},
  journal={Hugging Face Repository},
  url={https://huggingface.co/llm-jp/llm-jp-3-13b-instruct}
}

@article{marco-o1,
  title={Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions},
  author={Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang},
  year={2024},
  journal={arXiv},
  eprint={2411.14405v1},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

License

Refer to the base model's license at llm-jp/llm-jp-3-13b-instruct for details.

This README provides clear documentation on how to use the model while crediting its sources. Let me know if you need modifications!

doshisha-mil/llm-jp-13b-OpenMathInstruct_2_v1.1

llm-jp-13b-OpenWebMathInstruct_2_v1.1

Overview