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Model: noctrex/OpenThinker-Agent-v1-abliterated-GGUF Source: Original Platform
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README.md
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README.md
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---
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pipeline_tag: text-generation
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tags:
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- uncensored
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- abliterated
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base_model:
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- open-thoughts/OpenThinker-Agent-v1
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---
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This is an abliterated version of [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1), made using [Heretic](https://github.com/p-e-w/heretic) v1.0.1
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The quantizations were created using an imatrix merged from [combined\_en\_medium](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_en_medium.parquet) and [harmful.txt](https://github.com/Sumandora/remove-refusals-with-transformers) to leverage the abliterated nature of the model.
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## Performance
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| Metric | This model | [Original model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) |
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| :----- | :--------: | :---------------------------: |
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| **Refusals** | 3/100 | 99/100 |
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## Analysis against the original model:
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Detailed Analysis:
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- Total Tensors: 399
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- Tensors with Diffs: 202 (50.6%)
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- Average % Diff: 6.35%
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- Median % Diff: 0.00%
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- Min/Max % Diff: 0.00% / 46.22%
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- Std Dev % Diff: 15.56%
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- Skewness % Diff: 2.04
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- Avg L2 Norm: 125405.56
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- Tensors with >5% diff: 57
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- Top differences:
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blk.35.attn_output.weight ((4096, 8192), L2: 668013.65): 46.22%
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blk.34.ffn_down.weight ((4096, 24576), L2: 1155843.86): 46.07%
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blk.18.attn_output.weight ((4096, 8192), L2: 667142.18): 46.00%
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blk.16.ffn_down.weight ((4096, 24576), L2: 1154713.83): 45.95%
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blk.24.attn_output.weight ((4096, 8192), L2: 666019.48): 45.66%
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File Comparison:
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File 1: Avg Abs Value = 77.9178, Deviation Score = 0.0991
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File 2: Avg Abs Value = 77.9111, Deviation Score = 0.0991
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Positive Diffs (File 1 > File 2): 143, Negative Diffs (File 2 > File 1): 59
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## BibTeX entry and citation info
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```bibtex
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@misc{heretic,
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author = {Weidmann, Philipp Emanuel},
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title = {Heretic: Fully automatic censorship removal for language models},
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year = {2025},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/p-e-w/heretic}}
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}
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```
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# Original model card:
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<p align="center">
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<img src="https://huggingface.co/datasets/open-thoughts/OpenThoughts1-Agent-SFT/resolve/main/ota-logo.png" width="50%">
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</p>
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<p align="center">
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<a href="https://www.openthoughts.ai/blog/agent" style="margin-right: 24px;">Project</a> |
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<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT dataset</a> |
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<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL" style="margin-right: 24px; margin-left: 24px;">RL dataset</a> |
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<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT model</a> |
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<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1" style="margin-left: 24px;">RL model</a>
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</p>
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# OpenThinker-Agent-v1
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**OpenThoughts-Agent** is an open-source effort to curate the best datasets for training agents. Our first release includes [datasets](https://huggingface.co/collections/open-thoughts/openthinker-agent), [models](https://huggingface.co/collections/open-thoughts/openthinker-agent) and our [research codebase](https://github.com/open-thoughts/OpenThoughts-Agent).
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[OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) is a model trained for agentic tasks such as **Terminal-Bench 2.0** and **SWE-Bench**.
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The [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) model is post-trained from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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It is SFT-ed on the [OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) dataset, then RL-ed on the [OpenThoughts-Agent-v1-RL](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) dataset.
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This model is the final model after both SFT and RL. For the model after the SFT stage only, see [OpenThinker-Agent-v1-SFT](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT).
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- **Homepage:** https://www.openthoughts.ai/blog/agent
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- **Repository:** https://github.com/open-thoughts/OpenThoughts-Agent
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# OpenThinker-Agent-v1 Model Performance
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Our [OpenThinker-Agent-v1](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) model is the state-of-the-art model at its scale on agent benchmarks.
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| Model | Harness | Terminal-Bench 2.0 | SWE-Bench Verified | OpenThoughts-TB-Dev |
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| ----------------------------------------------------------------------------------------------- | ------- | ------------------ | --------- | ------------------- |
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| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) | Terminus-2 | 0.0 | 0.7 | 5.7 |
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| **[OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)** | Terminus-2 | 4.9 | 15.7 | 17.3 |
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| [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | Terminus-2 | 1.9 | 5.7 | 10.2 |
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| [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) | OpenHands | 10.1 | 49.2 | 24.5 |
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# Data
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We built [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) in two stages: **supervised fine-tuning**, followed by **reinforcement learning**.
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Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.
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[OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) is an SFT trace dataset containing approximately **15,200 traces** drawn from two different data sources we curate:
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- **nl2bash**: Simple synthetically generated tasks where the agent has to format shell commands effectively
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- **InferredBugs**: A set of bugs in C# and Java collected by Microsoft that we turned into tasks
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[OpenThoughts-Agent-v1-RL](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) is an RL dataset containing ~720 tasks drawn from the **nl2bash verified** dataset.
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To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:
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1. Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
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2. Environment stability: remove tasks whose containers take too long to build or tear down.
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Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.
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# Links
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- 🌐 [OpenThoughts-Agent project page](https://www.openthoughts.ai/blog/agent)
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- 💻 [OpenThoughts-Agent GitHub repository](https://github.com/open-thoughts/OpenThoughts-Agent)
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- 🧠 [OpenThoughts-Agent-v1-SFT dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT)
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- 🧠 [OpenThoughts-Agent-v1-RL dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL)
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- 🧠 [OpenThoughts-TB-dev dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TB-dev)
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- 🤖 [OpenThinker-Agent-v1 model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)
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- 🤖 [OpenThinker-Agent-v1-SFT model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT)
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# Citation
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```
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@misc{openthoughts-agent,
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author = {Team, OpenThoughts-Agent},
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month = Dec,
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title = {{OpenThoughts-Agent}},
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howpublished = {https://open-thoughts.ai/agent},
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year = {2025}
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}
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```
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