rwlinno
/

topoprm-ckpts

@@ -1,209 +1,63 @@
 ---
-base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 - lora
-- sft
-- transformers
-- trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 ---
+license: mit
 tags:
 - lora
+- math-reasoning
+- process-reward-model
+- topology
+language:
+- en
+- zh
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+pipeline_tag: text-generation
 ---
+# TopoPRM Checkpoints
+LoRA adapters trained with TopoPRM (Topology-Aware Process Rewards) for mathematical reasoning.
+Paper: EMNLP 2026 (ARR May cycle)
+Code: https://github.com/RWLinno/TopoPRM
+## Available Checkpoints
+| Subdirectory | Base Model | Method | Steps | Key Results |
+|-------------|-----------|--------|-------|-------------|
+| `sft-dr1-7b-final` | DeepSeek-R1-Distill-Qwen-7B | SFT | 3651 | GSM8K 83.5% baseline |
+| `grpo-topoprm-dr1-7b` | DeepSeek-R1-Distill-Qwen-7B | GRPO+TopoPRM | 100 | Hierarchical reward |
+| `grpo-topoprm-qwen35-9b` | Qwen3.5-9B | GRPO+TopoPRM | 50 | GSM8K 93.5%, MATH500 49.8% |
+| `opd-topoprm-dr1-7b-v2` | DeepSeek-R1-Distill-Qwen-7B | OPD Stage3 | 200 | MATH500 60.8%, Omni-MATH 56.9% |
+| `opd-topoprm-qwen35-9b-v2` | Qwen3.5-9B | OPD Stage3 | 50 | Distillation |
+| `grpo-scae-qwen35-9b` | Qwen3.5-9B | GRPO+SCAE | 949 | SCAE variant |
+## Common LoRA Config
+All adapters share:
+- **r**: 64
+- **alpha**: 128
+- **dropout**: 0.05
+- **target_modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **task_type**: CAUSAL_LM
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
+model = PeftModel.from_pretrained(base_model, "rwlinno/topoprm-ckpts", subfolder="grpo-topoprm-dr1-7b")
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
+```
+## Citation
+```bibtex
+@inproceedings{topoprm2026,
+  title={Topology-Aware Process Rewards for Verifiable Mathematical Reasoning},
+  author={Weilin Ruan},
+  booktitle={Proceedings of EMNLP 2026},
+  year={2026}
+}
+```