noctrex/OpenThinker-Agent-v1-abliterated-GGUF

This is an abliterated version of OpenThinker-Agent-v1, made using Heretic v1.0.1

The quantizations were created using an imatrix merged from combined_en_medium and harmful.txt to leverage the abliterated nature of the model.

Performance

Metric	This model	Original model
Refusals	3/100	99/100

Analysis against the original model:

Detailed Analysis:

• Total Tensors: 399
• Tensors with Diffs: 202 (50.6%)
• Average % Diff: 6.35%
• Median % Diff: 0.00%
• Min/Max % Diff: 0.00% / 46.22%
• Std Dev % Diff: 15.56%
• Skewness % Diff: 2.04
• Avg L2 Norm: 125405.56
• Tensors with >5% diff: 57
• Top differences: blk.35.attn_output.weight ((4096, 8192), L2: 668013.65): 46.22% blk.34.ffn_down.weight ((4096, 24576), L2: 1155843.86): 46.07% blk.18.attn_output.weight ((4096, 8192), L2: 667142.18): 46.00% blk.16.ffn_down.weight ((4096, 24576), L2: 1154713.83): 45.95% blk.24.attn_output.weight ((4096, 8192), L2: 666019.48): 45.66%

File Comparison: File 1: Avg Abs Value = 77.9178, Deviation Score = 0.0991 File 2: Avg Abs Value = 77.9111, Deviation Score = 0.0991 Positive Diffs (File 1 > File 2): 143, Negative Diffs (File 2 > File 1): 59

BibTeX entry and citation info

@misc{heretic,
  author = {Weidmann, Philipp Emanuel},
  title = {Heretic: Fully automatic censorship removal for language models},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/p-e-w/heretic}}
}

Original model card:

Project | SFT dataset | RL dataset | SFT model | RL model

OpenThinker-Agent-v1

OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes datasets, models and our research codebase.

OpenThinker-Agent-v1 is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.

The OpenThinker-Agent-v1 model is post-trained from Qwen/Qwen3-8B. It is SFT-ed on the OpenThoughts-Agent-v1-SFT dataset, then RL-ed on the OpenThoughts-Agent-v1-RL dataset.

This model is the final model after both SFT and RL. For the model after the SFT stage only, see OpenThinker-Agent-v1-SFT.

• Homepage: https://www.openthoughts.ai/blog/agent
• Repository: https://github.com/open-thoughts/OpenThoughts-Agent

OpenThinker-Agent-v1 Model Performance

Our OpenThinker-Agent-v1 model is the state-of-the-art model at its scale on agent benchmarks.

Model	Harness	Terminal-Bench 2.0	SWE-Bench Verified	OpenThoughts-TB-Dev
Qwen3-8B	Terminus-2	0.0	0.7	5.7
OpenThinker-Agent-v1	Terminus-2	4.9	15.7	17.3
Qwen3-32B	Terminus-2	1.9	5.7	10.2
Qwen/Qwen3-Coder-30B-A3B-Instruct	OpenHands	10.1	49.2	24.5

Data

We built OpenThinker-Agent-v1 in two stages: supervised fine-tuning, followed by reinforcement learning. Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.

OpenThoughts-Agent-v1-SFT is an SFT trace dataset containing approximately 15,200 traces drawn from two different data sources we curate:

• nl2bash: Simple synthetically generated tasks where the agent has to format shell commands effectively
• InferredBugs: A set of bugs in C# and Java collected by Microsoft that we turned into tasks

OpenThoughts-Agent-v1-RL is an RL dataset containing ~720 tasks drawn from the nl2bash verified dataset.

To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:

1. Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
2. Environment stability: remove tasks whose containers take too long to build or tear down. Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.

Links

• 🌐 OpenThoughts-Agent project page
• 💻 OpenThoughts-Agent GitHub repository
• 🧠 OpenThoughts-Agent-v1-SFT dataset
• 🧠 OpenThoughts-Agent-v1-RL dataset
• 🧠 OpenThoughts-TB-dev dataset
• 🤖 OpenThinker-Agent-v1 model
• 🤖 OpenThinker-Agent-v1-SFT model

Citation

@misc{openthoughts-agent,
  author = {Team, OpenThoughts-Agent},
  month = Dec,
  title = {{OpenThoughts-Agent}},
  howpublished = {https://open-thoughts.ai/agent},
  year = {2025}
}