commit 92b266ca435bcf57b5db8262905318735e2a4a45 Author: ModelHub XC Date: Sun May 10 19:21:23 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ryokamoi/Llama-3.1-8B-FoVer-PRM-old Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..9ca6bf7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +readme_figures/fover_overview.png filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..89b8a06 --- /dev/null +++ b/README.md @@ -0,0 +1,211 @@ +--- +language: +- en +license: llama3.1 +base_model: meta-llama/Llama-3.1-8B-Instruct +pipeline_tag: text-generation +tags: +- reward model +--- + +**This repository contains materials for a previous version of the paper. Please refer to the [latest version](https://github.com/psunlpgroup/FoVer).** + +# FoVer + +

+Project Website | 📄 Paper | 🛠️ GitHub | 🤗 Dataset | 🤗 Models +

+ +This repository includes code and materials for the paper "Efficient PRM Training Data Synthesis via Formal Verification". + +Please refer to [Quick Start](#quick-start) for a quick start guide to evaluate your models on the FoVer dataset or evaluate the FoVer models on your dataset. + +* GitHub: [https://github.com/psunlpgroup/FoVer](https://github.com/psunlpgroup/FoVer) +* FoVer Dataset + * Raw datasets (including the training, validation, and test splits) + * [ryokamoi/FoVer-FormalLogic-Llama-3.1-8B](https://huggingface.co/datasets/ryokamoi/FoVer-FormalLogic-Llama-3.1-8B) + * [ryokamoi/FoVer-FormalProof-Llama-3.1-8B](https://huggingface.co/datasets/ryokamoi/FoVer-FormalProof-Llama-3.1-8B) + * [ryokamoi/FoVer-FormalLogic-Qwen-2.5-7B](https://huggingface.co/datasets/ryokamoi/FoVer-FormalLogic-Qwen-2.5-7B) + * [ryokamoi/FoVer-FormalProof-Qwen-2.5-7B](https://huggingface.co/datasets/ryokamoi/FoVer-FormalProof-Qwen-2.5-7B) + * Balanced datasets for training (including training data only) + * [ryokamoi/FoVer-FormalLogic-FormalProof-Llama-3.1-8B-LastStepBalanced-40k](https://huggingface.co/datasets/ryokamoi/FoVer-FormalLogic-FormalProof-Llama-3.1-8B-LastStepBalanced-40k) + * [ryokamoi/FoVer-FormalLogic-FormalProof-Qwen-2.5-7B-LastStepBalanced-40k](https://huggingface.co/datasets/ryokamoi/FoVer-FormalLogic-FormalProof-Qwen-2.5-7B-LastStepBalanced-40k) + +* FoVer PRMs + * [ryokamoi/Llama-3.1-8B-FoVer-PRM](https://huggingface.co/ryokamoi/Llama-3.1-8B-FoVer-PRM) + * [ryokamoi/Qwen-2.5-7B-FoVer-PRM](https://huggingface.co/ryokamoi/Qwen-2.5-7B-FoVer-PRM) + +* Other materials, including variants of the datasets and intermediate outputs + * [ryokamoi/FoVer-misc](https://huggingface.co/datasets/ryokamoi/FoVer-misc) + + +```bibtex +@inproceedings{kamoi2025fover, + title = {Efficient PRM Training Data Synthesis via Formal Verification}, + author = {Ryo Kamoi and Yusen Zhang and Nan Zhang and Sarkar Snigdha Sarathi Das and Ranran Haoran Zhang and Wenpeng Yin and Rui Zhang}, + year = {2026}, + booktitle = {Findings of the Association for Computational Linguistics: ACL 2026}, +} +``` + +## Introduction + +Process reward models (PRMs), which provide step-by-step feedback on the reasoning generated by large language models (LLMs), are receiving increasing attention for their potential to enhance LLMs via reinforcement learning and inference-time refinement. + +We propose FoVer, an approach for training PRMs on step-level error labels that are automatically annotated using formal verification tools (e.g., Z3, Isabelle). We introduce a dataset that includes automatically annotated step-level error labels on LLM responses for the formal logic and proof tasks. We demonstrate that LLM-based PRMs trained on the FoVer dataset exhibit cross-task transfer of verification capabilities learned in formal logic and proof, leading to improved verification across a broad range of reasoning tasks, including mathematics, academic problems, logic, and abstract reasoning. + +
+ + +## Setup + +To run our PRMs: + +* torch==2.6.0 +* transformers==4.50.3 + +Please refer to [setup/setup.sh](https://github.com/psunlpgroup/FoVer/setup/setup.sh) for details. We use different environments for dataset creation, training, and evaluation. + +We run our experiments on the following environment. You might need to modify configulations if you are using a different environment. + +* Four NVIDIA A100 SXM4 80GB GPUs +* CUDA Version: 12.2 + +## Quick Start + +### Evaluate Your PRM on the FoVer Datasets + +The FoVer dataset is initially designed to train models, but our test splits also serves as an evaluation benchmark for PRMs. Our dataset provides the following information. Please refer to [FoVer Dataset](#fover-dataset) for details of other items in our dataset. + +```json +{ + "problem": """Based on the provided facts ($context$), either prove or disprove the hypothesis or state that it is unknown. The facts and the hypothesis are written in logical formulas as follows: capital letters such as "{A}", "{B}", "{AB}" are predicates, small letters such as "{a}", "{b}", "{ab}" are constants, "&" is logical conjunction, "v" is logical disjunction, "¬" is negation, "->" is implication, "(x)" is "for all x", and "(Ex)" is "for some x".\n\n$hypothesis$: ¬{A}\n\n$context$:\nfact1: {IN}\nfact2: {BH}\nfact3: {EE}\nfact4: ¬{B} -> ({A} & {FH})\nfact5: {CA}\nfact6: {GO}\nfact7: {IR}\nfact8: {HH}\nfact9: {JI}\nfact10: {AN}\nfact11: {C} -> ({B} & ¬{A})\nfact12: {HP}\nfact13: {GK}\nfact14: {JC}\nfact15: ¬{E} -> ({C} & {D})\nfact16: {T}\nfact17: {H}\nfact18: {AF}""", + "solution_steps": [ + "fact11 -> int1: {B} & ¬{A}", + "int1 -> int2: ¬{A}", + "The final answer is PROVED" + ], + "error_labels": [false, true, true] +} +``` + +You can access our dataset from Hugging Face Hub. + +```python +from datasets import load_dataset + +dataset = load_dataset("ryokamoi/FoVer-FormalLogic-Qwen-2.5-7B", split="validation") + +print(dataset[0].keys()) +# dict_keys(['id', 'problem', 'solution_steps', 'error_labels', +# 'problem_witout_definition', 'messages', 'base_dataset', +# 'messages_for_prediction', 'hypothesis_formula', 'facts_formula']) + +print(dataset[0]['error_labels']) +# [True, True, True, True, True, False, True, False] +``` + +### Evaluate the FoVer PRMs on Your Dataset + +Here is the minimum example to run FoVer PRMs. Please clone our GitHub repository to use the post-processing functions. + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +from src.prm.preprocessing import get_fover_input_format +from src.prm.postprocessing import extract_fover_scores + +# ryokamoi/Qwen-2.5-7B-FoVer-PRM or +# ryokamoi/Llama-3.1-8B-FoVer-PRM +prm_name = "ryokamoi/Qwen-2.5-7B-FoVer-PRM" + +tokenizer = AutoTokenizer.from_pretrained(prm_name) +model = AutoModelForCausalLM.from_pretrained(prm_name).to("cuda") + +# Get input format for the FoVer PRM +conversation = get_fover_input_format( + problem="Calculate (1+1)*(1+2)", + solution_steps=["1+1=2", "1+2=3", "2*3=8"], +) +inputs = tokenizer.apply_chat_template( + conversation, return_tensors="pt").to("cuda") + +# Generate the step-level scores +output = model(inputs) + +# extract the step-level scores +scores = extract_fover_scores( + tokenized_prompt=inputs[0].cpu().numpy(), + logits=output.logits[0], + tokenizer=tokenizer, +) + +print(scores) +# [0.9099470376968384, 0.9997847676277161, 0.012338237836956978] +``` + +We also provide a script to evaluate the FoVer PRMs on your dataset. + +First, convert your dataset into a JSONL file whose rows are in the following format and put at [quickstart/dataset/testdata.jsonl](https://github.com/psunlpgroup/FoVer/quickstart/dataset/testdata.jsonl). + +```json +{"problem": "this is a problem.", "solution_steps": ["first step (correct)", "second step (wrong)", "third step (unknown)"], "error_labels": [true, false, null]} +``` + +Then, run the following command to evaluate the PRM on your dataset. We use the minimum step-level score as an instance-level score by default. + +```bash +python quickstart/evaluate.py \ + --fover_prm_name ryokamoi/Qwen-2.5-7B-FoVer-PRM \ + --dataset_dir quickstart/dataset/test_data \ + --output_dir quickstart/results/ +``` + +You will get the following outputs. + +* `quickstart/results/testdata/performance.json` + * The performance metrics of the FoVer PRM on your dataset. + * The step-level and instance-level scores by the FoVer PRM on your dataset. + +## FoVer Dataset + +We provide the FoVer datasets that include the mistakes made by Llama 3.1 8B and Qwen 2.5 7B on formal logic and proof tasks. + +### Dataset Format + +Each instance of the FoVer datasets include the following items. + +* `problem` (str) +* `solution_steps` (list[str]) + * The solution steps generated by the model. +* `error_labels` (list[str]) + * The ground-truth error labels generated by the error verification tools (Z3, Isabelle) +* `messages` (list[dict[str, str]]) + * The conversation we use for fine-tuning our PRMs. +* `messages_for_prediction` (list[dict[str, str]]) + * The conversation we use for prediction. The model outputs are dummy values and all `correct`. +* `problem_witout_definition` (str) + * The `problem` without task definition (metadata, not used in our experiments). + +### Dataset Statistics + +
+ +### LastStepBalanced Dataset + +We create the LastStepBalanced dataset to train PRMs on the balanced dataset where the last step includes 50% of correct and 50% of incorrect steps. We truncate solutions to make the last step balanced, so we expect to mask all steps but the last step to train the PRMs. + +Specificlaly, we use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) with the option `mask_history: true`. + +### Creating Training Data for New Models + +You can create mistakes made by stronger models to make a better training dataset. Please refer to [run/01_dataset_creation](run/01_dataset_creation) for the dataset creation process. You may need to update our code to support other models. + +## Reproducing the Experiments in the Paper + +You can refer to shell files in the [run](run) directory to reproduce the experiments in our paper. + +You do not need to run the code if you are only interested in using our models or datasets. Please refer to [Quick Start](#quick-start). + +## License + +Please refer to the [LICENSE.md](https://github.com/psunlpgroup/FoVer/LICENSE.md) file for the license of this repository. diff --git a/config.json b/config.json new file mode 100644 index 0000000..46a4f40 --- /dev/null +++ b/config.json @@ -0,0 +1,39 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" 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"rstrip": false, + "single_word": false, + "special": true + }, + "128251": { + "content": "<|reserved_special_token_243|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128252": { + "content": "<|reserved_special_token_244|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128253": { + "content": "<|reserved_special_token_245|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128254": { + "content": "<|reserved_special_token_246|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128255": { + "content": "<|reserved_special_token_247|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|eot_id|>", + "<|eom_id|>" + ], + "bos_token": "<|begin_of_text|>", + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|eot_id|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|eot_id|>", + "padding_side": "right", + "split_special_tokens": false, + "tokenizer_class": "PreTrainedTokenizer" +}