add readme

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Agentica
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -2,6 +2,8 @@
 library_name: peft
 license: other
 base_model: Qwen/Qwen3-14B
+datasets:
+- r2e-edits/deepswe-swebv-eval-n16-verifier-v1
 tags:
 - llama-factory
 - lora
@@ -14,25 +16,48 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# verifier
-
-This model is a fine-tuned version of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) on the deepswe-verifier-exitreason-agent-priority-v1 dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+<div align="center">
+<span style="font-family: default; font-size: 1.5em;">DeepSWE-Verifier</span>
+<div>
+🚀 Democratizing Reinforcement Learning for LLM Agents (RLLM) 🌟
+</div>
+</div>
+<br>
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/agentica-project/rllm" style="margin: 2px;">
+    <img alt="Code" src="https://img.shields.io/badge/rLLM-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="www.google.com" target="_blank" style="margin: 2px;">
+    <img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://x.com/Agentica_" style="margin: 2px;">
+    <img alt="X.ai" src="https://img.shields.io/badge/Agentica-white?style=for-the-badge&logo=X&logoColor=000&color=000&labelColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/agentica-org" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/Agentica-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+</div>
+</div>
+
+## DeepSWE-Verifier Overview
+DeepSWE-Verifier is "critic model" that aids DeepSWE-Preview, a coding agent, for test-time scaling. For each SWE-Bench problem, DeepSWE-Preview generates multiple solutions, which produces multiple code patches, while DeepSWE-Verifier chooses the best code patch.Pairing DeepSWE-Preview with DeepSWE-Verifier can increases SWE-Bench-Verified score by +10% (See Figure 1, Execution-Free Verifier).
+
+DeepSWE-Verifier is a fine-tuned/SFT version of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) 
+
+Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](www.google.com).
+
+<div style="margin: 0 auto;">
+  <img src="https://cdn-lfs-us-1.hf.co/repos/fe/8c/fe8cf2197ba6bcf2ded6d3e131c2688d33f84166d98d6faf3da79cf572a06253/f6503dea8049dd709774c1f6cd1837867f5756ec82c24306a91834e30f66e767?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27bestk_plot_agent.png%3B+filename%3D%22bestk_plot_agent.png%22%3B&response-content-type=image%2Fpng&Expires=1751412784&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc1MTQxMjc4NH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zL2ZlLzhjL2ZlOGNmMjE5N2JhNmJjZjJkZWQ2ZDNlMTMxYzI2ODhkMzNmODQxNjZkOThkNmZhZjNkYTc5Y2Y1NzJhMDYyNTMvZjY1MDNkZWE4MDQ5ZGQ3MDk3NzRjMWY2Y2QxODM3ODY3ZjU3NTZlYzgyYzI0MzA2YTkxODM0ZTMwZjY2ZTc2Nz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=HqZtKWygmax5Doo9Sdj09-PeBonl1P5%7ErphhEy01Ry9FPtLN3kKublSGf7uufQzoLdMT9yQJep4MI9WcxTliyCJ2MZqyKC4jfoRaLuRxOvD4TB54TtsZ6ATknvLmXtzcg0uEiZc%7E75IP2aTNk4RVfK211N5pj6u1rZTF45vC9c7xojldEXDLHKoW9zKQx695ULxTtYOHgq3BPexZ4LcOP0AUyTIDOxyEPFeV0jRUNkrGlb7qi3Xbcav6I5jd9HEgJPwioqK2s4JR4HktQS7oOLIrgFuNtjktOU8ReHzb92o7M7SqMWhn37wDU9gMgYui60uArDuTdmkcXCxZolwZVA__&Key-Pair-Id=K24J24Z295AEI9" style="width: 100%;" />
+  <p align="center" style="margin-top: 8px; font-style: italic; color: #666;">
+    Figure 1: SWE-Bench Verified Performance w.r.t. different TTS strategies. With hybrid TTS, DeepSWE-Preview achieves 59%, beating the current SOTA open-weights model (SkyWork + TTS, 47%) by 12%. We note that only using execution-based and execution-free verifiers is still effective and can bring 10+% performance.
+  </p>
+</div>
+
+
+## Training 
+
+### Hyperparameters 
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
@@ -48,10 +73,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.05
 - num_epochs: 2.0
 
-### Training results
-
-
-
 ### Framework versions
 
 - PEFT 0.12.0