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Mocha-Coder-32B

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  1. .gitattributes +1 -0
  2. README.md +208 -0
  3. added_tokens.json +24 -0
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  5. config.json +99 -0
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  23. special_tokens_map.json +31 -0
  24. tokenizer.json +3 -0
  25. tokenizer_config.json +207 -0
  26. vocab.json +0 -0
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README.md ADDED
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+ ---
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+ base_model:
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+ - Qwen/Qwen2.5-Coder-32B-Instruct
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+ language:
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+ - en
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+ license: mit
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+ pipeline_tag: text-generation
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+ tags:
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+ - code
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+ - coding-agent
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+ - SWE-agent
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+ - distillation
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+ - agent
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+ library_name: transformers
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+ ---
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+
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+ <h1 style="
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+ font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Helvetica,Arial,sans-serif;
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+ font-size:48px;
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+ font-weight:700;
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+ line-height:1.25;
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+ text-align:center;
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+ margin:0 0 24px;">
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+ Mocha-Coder-32B
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+ </h1>
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+
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+ <p style="text-align:center; margin:0 0 8px; font-size:16px;">
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+ <a href="https://junliwang.tech/">Junli Wang</a><sup>*</sup> &nbsp;
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+ <a href="https://blankcheng.github.io/">Zhoujun Cheng</a><sup>*†</sup> &nbsp;
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+ <a href="https://yuxuan-zhang-dexter.github.io/">Yuxuan Zhang</a><sup>*</sup> &nbsp;
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+ <a href="https://ber666.github.io/">Shibo Hao</a> &nbsp;
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+ <a href="https://yaotang23.github.io/">Yao Tang</a> &nbsp;
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+ <br>
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+ <a href="https://zhiting.ucsd.edu/">Zhiting Hu</a> &nbsp;
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+ <a href="https://prithvirajva.com/">Prithviraj Ammanabrolu</a> &nbsp;
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+ <a href="https://haozhang.ai/">Hao Zhang</a><sup>†</sup>
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+ </p>
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+
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+ <p style="text-align:center; margin:0 0 24px; font-size:14px; color:#555;">
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+ University of California, San Diego &nbsp;·&nbsp;
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+ <sup>*</sup>Equal Contribution &nbsp;·&nbsp;
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+ <sup>†</sup>Corresponding Author
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+ </p>
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+
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+ <div style="
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+ display:flex;
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+ justify-content:center;
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+ gap:12px;
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+ flex-wrap:wrap;
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+ margin-bottom:28px;">
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+
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+ <a href="https://github.com/cocoa-org/NanoRollout" style="
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+ display:inline-block;
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+ padding:8px 24px;
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+ background:#2b2b2b;
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+ color:#ffffff;
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+ border-radius:36px;
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+ text-decoration:none;
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+ font-weight:600;
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+ font-size:16px;">
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+ 🧑‍💻 NanoRollout Code
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+ </a>
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+
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+ <a href="https://huggingface.co/ZeonLap/Mocha-Coder-32B" style="
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+ display:inline-block;
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+ padding:8px 24px;
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+ color:#ffffff;
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+ border-radius:36px;
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+ text-decoration:none;
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+ font-weight:600;
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+ font-size:16px;">
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+ 🤗 Mocha-Coder-32B Model
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+ </a>
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+
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+ <a href="https://cocoa-org.notion.site/nanorollout" style="
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+ display:inline-block;
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+ padding:8px 24px;
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+ background:#2b2b2b;
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+ color:#ffffff;
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+ border-radius:36px;
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+ text-decoration:none;
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+ font-weight:600;
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+ font-size:16px;">
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+ 📒 Blog
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+ </a>
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+ </div>
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+
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+ <div style="max-width:900px;margin:0 auto;">
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+
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+ # Introduction
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+ <div style="
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+ max-width: 880px;
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+ margin: 0 auto;
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+ text-align: justify;
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+ text-justify: inter-word;
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+ line-height: 1.6;">
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+
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+ **Mocha-Coder-32B** is a strong open-data coding agent built on top of **Qwen2.5-Coder-32B-Instruct**. It is trained entirely through distillation on a 300K+ trajectory mixture sampled with our lightweight agent-rollout infrastructure, **NanoRollout**, with no reinforcement learning. The full training signal comes from frontier open-source teacher models (Qwen3-Coder-480B-A35B, Kimi-K2.5, Qwen3-Coder-Next, DeepSeek-V3.2) generating trajectories across multiple agent harnesses (OpenHands, mini-swe-agent, Terminus-2 JSON) on SWE-Rebench, SWE-Smith, and SETA.
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+
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+ The result is a simple but strong baseline coding agent: at the ≤32B scale, Mocha-Coder-32B is the state-of-the-art among open-data models and is competitive with much larger open-source models on agentic SWE benchmarks.
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+ </div>
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+
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+ ### Key Features
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+
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+ - **Strong agentic SWE performance**: 62.6 Pass@1 on SWE-Bench Verified, 35.3 on SWE-Bench Pro, 23.6 on Terminal-Bench 2.0, competitive with Qwen3-Coder-480B-A35B-Instruct.
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+ - **Multi-harness training**: Trajectories cover OpenHands, mini-swe-agent, and Terminus-2 JSON, mitigating harness-specific overfitting.
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+ - **Open data**: Distilled from a fully released 300K+ trajectory mixture (`ZeonLap/Mocha-trajectories`).
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+
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+ # Performance
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+
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+ ### SWE-Bench Verified
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+ <div align="center">
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+
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+ | **Model** | **Max Iteration** | **SWE-Bench Verified (Pass@1)** |
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+ |----------------------------------|:-----------------:|:-------------------------------:|
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+ | Qwen3-Coder-480B-A35B-Instruct | 100 | 67.0 |
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+ | **Mocha-Coder-32B** | 100 | **62.6** |
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+ | SWE-Master-32B-RL | 150 | 61.4 |
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+ | Kimi-Dev-72B | Agentless, TTS@40 | 60.4 |
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+ | CoderForge-Preview-32B | 100 | 59.4 |
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+ | GLM-4.7-Flash | 100 | 59.2 |
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+ | daVinci-Dev-72B | 100 | 58.5 |
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+ | daVinci-Dev-32B | 100 | 56.1 |
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+ | SERA-32B | 100 | 54.2 |
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+ | Qwen3-Coder-30B-A3B-Instruct | 100 | 51.6 |
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+ | Qwen2.5-Coder-32B-Instruct (Base)| 100 | 6.2 |
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+ </div>
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+
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+ ### SWE-Bench Pro
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+ <div align="center">
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+
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+ | **Model** | **Max Iteration** | **SWE-Bench Pro (Pass@1)** |
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+ |----------------------------------|:-----------------:|:--------------------------:|
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+ | Qwen3-Coder-480B-A35B-Instruct | 250 | 38.7 |
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+ | **Mocha-Coder-32B** | 250 | **35.3** |
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+ | Gemini-3-flash | 250 | 34.6 |
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+ | Kimi-K2-Instruct | 250 | 27.7 |
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+ | DeepSeek-V3.2 | 250 | 15.6 |
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+ | Qwen2.5-Coder-32B-Instruct (Base)| 250 | 0.0 |
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+ </div>
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+
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+ ### Terminal-Bench 2.0
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+ <div align="center">
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+
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+ | **Model** | **Terminal-Bench 2.0** |
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+ |----------------------------------|:----------------------:|
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+ | Qwen3-Coder-480B-A35B-Instruct | 23.9 |
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+ | **Mocha-Coder-32B** | **23.6** |
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+ | Qwen3-Coder-30B-A3B-Instruct | 13.5 |
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+ | Qwen2.5-Coder-32B-Instruct (Base)| 3.4 |
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+ </div>
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+
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+ # Training Data
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+
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+ Mocha-Coder-32B is trained on a **300K+ trajectory** distillation mixture, drawn from previously released distillation sets (120K) and trajectories newly generated with NanoRollout (~180K).
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+
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+ | **Dataset** | **Teacher Model** | **Harness** | **# Trajectories (K)** | **Source** |
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+ |-----------------|-----------------------------|-------------------|:----------------------:|-------------------|
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+ | SWE-Rebench | Qwen3-Coder-480B-A35B | OpenHands | 32.2 | Nebius |
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+ | SWE-Smith | Qwen3-Coder-480B-A35B | OpenHands | 89.5 | CoderForge |
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+ | SWE-Rebench | Kimi-K2.5 | mini-swe-agent | 83.6 | NanoRollout |
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+ | SWE-Rebench | Qwen3-Coder-Next | mini-swe-agent | 11.5 | NanoRollout |
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+ | SWE-Smith | Qwen3-Coder-480B-A35B | mini-swe-agent | 12.8 | NanoRollout |
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+ | SWE-Smith | Qwen3-Coder-Next | mini-swe-agent | 9.1 | NanoRollout |
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+ | SETA | Kimi-K2.5 / DeepSeek-V3.2 | Terminus-2 JSON | 14.0 | NanoRollout |
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+
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+ The full mixture is released at [`ZeonLap/Mocha-trajectories`](https://huggingface.co/datasets/ZeonLap/Mocha-trajectories).
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+
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+ # Running as an Agent
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+
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+ Mocha-Coder-32B is trained as an agent and is most useful when paired with a coding-agent harness. We have validated it with:
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+
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+ - **mini-swe-agent** — minimal SWE agent loop, recommended for SWE-Bench Verified / Pro evaluation.
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+ - **OpenHands** — full-featured SWE harness; the model was trained on OpenHands trajectories.
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+ - **Terminus-2 JSON** — for Terminal-Bench 2.0 style shell tasks.
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+
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+ Point each harness's model endpoint at the vLLM server above. For SWE-Bench Verified we report numbers at a 100-iteration budget; for SWE-Bench Pro at 250 iterations.
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+
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+ # License
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+
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+ Mocha-Coder-32B (model weights, training trajectories, and code) is released under the **MIT License** (see `LICENSE`) for research, educational, and commercial use.
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+
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+ # Citation
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+
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+ If you use Mocha-Coder-32B or NanoRollout in your research, please cite:
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+
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+ ```bibtex
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+ @misc{wang2026mochacoder32b,
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+ title = {Mocha-Coder-32B: Scaling Open-Data Coding Agents with NanoRollout},
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+ author = {Wang, Junli and Cheng, Zhoujun and Zhang, Yuxuan and Hao, Shibo
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+ and Tang, Yao and Hu, Zhiting and Ammanabrolu, Prithviraj
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+ and Zhang, Hao},
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+ year = {2026},
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+ howpublished = {\url{https://huggingface.co/ZeonLap/Mocha-Coder-32B}},
196
+ }
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+
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+ @misc{nanorollout,
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+ title = {NanoRollout: A Lightweight Infra for Digital Agent Rollout at Scale},
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+ author = {Wang, Junli and Cheng, Zhoujun and Zhang, Yuxuan and Hao, Shibo
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+ and Tang, Yao and Hu, Zhiting and Ammanabrolu, Prithviraj
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+ and Zhang, Hao},
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+ year = {2026},
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+ howpublished = {\url{https://github.com/cocoa-org/NanoRollout}},
205
+ }
206
+ ```
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+
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+ </div>
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