[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2448":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":30,"readmeContent":31,"aiSummary":32,"trendingCount":16,"starSnapshotCount":16,"syncStatus":33,"lastSyncTime":34,"discoverSource":35},2448,"heretic","p-e-w\u002Fheretic","p-e-w","Fully automatic censorship removal for language models","",null,"Python",24178,2581,97,56,0,79,531,3418,341,45,"GNU Affero General Public License v3.0",false,"master",true,[27,28,29],"abliteration","llm","transformer","2026-06-12 02:00:41","\u003Cimg width=\"128\" height=\"128\" align=\"right\" alt=\"Logo\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fdf5f2840-2f92-4991-aa57-252747d7182e\" \u002F>\n\n# Heretic: Fully automatic censorship removal for language models\u003Cbr>\u003Cbr>[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https:\u002F\u002Fdiscord.gg\u002FgdXc48gSyT) [![Follow us on Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fhuggingface\u002Fbadges\u002Fresolve\u002Fmain\u002Ffollow-us-on-hf-md-dark.svg)](https:\u002F\u002Fhuggingface.co\u002Fheretic-org) [![Codeberg mirror](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCodeberg%20mirror-black?logo=codeberg&style=for-the-badge)](https:\u002F\u002Fcodeberg.org\u002Fp-e-w\u002Fheretic)\n\n[![#1 Repository of the Day](https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F20538)](https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F20538)\n\nHeretic is a tool that removes censorship (aka \"safety alignment\") from\ntransformer-based language models without expensive post-training.\nIt combines an advanced implementation of directional ablation, also known\nas \"abliteration\" ([Arditi et al. 2024](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11717),\nLai 2025 ([1](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fgrimjim\u002Fprojected-abliteration),\n[2](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fgrimjim\u002Fnorm-preserving-biprojected-abliteration))),\nwith a TPE-based parameter optimizer powered by [Optuna](https:\u002F\u002Foptuna.org\u002F).\n\nThis approach enables Heretic to work **completely automatically.** Heretic\nfinds high-quality abliteration parameters by co-minimizing the number of\nrefusals and the KL divergence from the original model. This results in a\ndecensored model that retains as much of the original model's intelligence\nas possible. Using Heretic does not require an understanding of transformer\ninternals. In fact, anyone who knows how to run a command-line program\ncan use Heretic to decensor language models.\n\nHeretic supports most dense models, including many multimodal models,\nseveral different MoE architectures, and even some hybrid models like Qwen3.5.\nPure state-space models and certain other research architectures are not yet\nsupported out of the box.\n\n\u003Cimg width=\"650\" height=\"715\" alt=\"Screenshot\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fd71a5efa-d6be-4705-a817-63332afb2d15\" \u002F>\n\n \n\nRunning unsupervised with the default configuration, Heretic can produce\ndecensored models that rival the quality of abliterations created manually\nby human experts:\n\n| Model | Refusals for \"harmful\" prompts | KL divergence from original model for \"harmless\" prompts |\n| :--- | ---: | ---: |\n| [google\u002Fgemma-3-12b-it](https:\u002F\u002Fhuggingface.co\u002Fgoogle\u002Fgemma-3-12b-it) (original) | 97\u002F100 | 0 *(by definition)* |\n| [mlabonne\u002Fgemma-3-12b-it-abliterated-v2](https:\u002F\u002Fhuggingface.co\u002Fmlabonne\u002Fgemma-3-12b-it-abliterated-v2) | 3\u002F100 | 1.04 |\n| [huihui-ai\u002Fgemma-3-12b-it-abliterated](https:\u002F\u002Fhuggingface.co\u002Fhuihui-ai\u002Fgemma-3-12b-it-abliterated) | 3\u002F100 | 0.45 |\n| **[p-e-w\u002Fgemma-3-12b-it-heretic](https:\u002F\u002Fhuggingface.co\u002Fp-e-w\u002Fgemma-3-12b-it-heretic) (ours)** | **3\u002F100** | **0.16** |\n\nThe Heretic version, generated without any human effort, achieves the same\nlevel of refusal suppression as other abliterations, but at a much lower\nKL divergence, indicating less damage to the original model's capabilities.\n*(You can reproduce those numbers using Heretic's built-in evaluation functionality,\ne.g. `heretic --model google\u002Fgemma-3-12b-it --evaluate-model p-e-w\u002Fgemma-3-12b-it-heretic`.\nNote that the exact values might be platform- and hardware-dependent.\nThe table above was compiled using PyTorch 2.8 on an RTX 5090.)*\n\nOf course, mathematical metrics and automated benchmarks never tell the whole\nstory, and are no substitute for human evaluation. Models generated with\nHeretic have been well-received by users (links and emphasis added):\n\n> \"I was skeptical before, but I just downloaded\n> [**GPT-OSS 20B Heretic**](https:\u002F\u002Fhuggingface.co\u002Fp-e-w\u002Fgpt-oss-20b-heretic)\n> model and holy shit. It gives properly formatted long responses to sensitive topics,\n> using the exact uncensored words that you would expect from an uncensored model,\n> produces markdown format tables with details and whatnot. Looks like this is\n> the best abliterated version of this model so far...\"\n> [*(Link to comment)*](https:\u002F\u002Fold.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1oymku1\u002Fheretic_fully_automatic_censorship_removal_for\u002Fnp6tba6\u002F)\n\n> \"[**Heretic GPT 20b**](https:\u002F\u002Fhuggingface.co\u002Fp-e-w\u002Fgpt-oss-20b-heretic)\n> seems to be the best uncensored model I have tried yet. It doesn't destroy a\n> the model's intelligence and it is answering prompts normally would be\n> rejected by the base model.\"\n> [*(Link to comment)*](https:\u002F\u002Fold.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1oymku1\u002Fheretic_fully_automatic_censorship_removal_for\u002Fnpe9jng\u002F)\n\n> \"[[**Qwen3-4B-Instruct-2507-heretic**](https:\u002F\u002Fhuggingface.co\u002Fp-e-w\u002FQwen3-4B-Instruct-2507-heretic)]\n> Has been the best unquantized abliterated model that I have been able to run on 16gb vram.\"\n> [*(Link to comment)*](https:\u002F\u002Fold.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1phjxca\u002Fim_calling_these_people_out_right_now\u002Fnt06tji\u002F)\n\nHeretic models have also been independently benchmarked using standard metrics\nlike MMLU and GSM8K, and have been found to compare favorably with models\nproduced by competing abliteration tools:\n[1](https:\u002F\u002Fold.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1sojjoc\u002Fabliterlitics_benchmark_and_tensor_analysis\u002F),\n[2](https:\u002F\u002Fold.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1sy18lx\u002Fabliterlitics_benchmarks_and_tensor_comparison\u002F).\n\nThe community has created and published\n[well over 3000](https:\u002F\u002Fhuggingface.co\u002Fmodels?other=heretic)\nmodels with Heretic.\n\n\n## Usage\n\nPrepare a Python 3.10+ environment with PyTorch 2.2+ installed as appropriate\nfor your hardware. Then run:\n\n```\npip install -U heretic-llm\nheretic Qwen\u002FQwen3-4B-Instruct-2507\n```\n\nReplace `Qwen\u002FQwen3-4B-Instruct-2507` with whatever model you want to decensor.\n\n> [!IMPORTANT]\n>\n> While PyTorch 2.2 is the minimum version of PyTorch needed for Heretic to work,\n> some models and configurations might require features only found in\n> later versions. For example, loading MXFP4-quantized models like gpt-oss\n> uses `torch.accelerator`, which was added in PyTorch 2.6.\n\n> [!TIP]\n>\n> Heretic uses [uv](https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002F) for dependency management,\n> and the repository includes a `uv.lock` file pinning every package version.\n> If you already use uv (and you probably should!), you can just clone the repo\n> and run Heretic with `uv run heretic`, which ensures that your dependencies\n> match those used by the developers, improving reliability and security.\n\nThe process is fully automatic and does not require configuration; however,\nHeretic has a variety of configuration parameters that can be changed for\ngreater control. Run `heretic --help` to see available command-line options,\nor look at [`config.default.toml`](config.default.toml) if you prefer to use\na configuration file.\n\nAt the start of a program run, Heretic benchmarks the system to determine\nthe optimal batch size to make the most of the available hardware.\nOn an RTX 3090, with the default configuration, decensoring Llama-3.1-8B-Instruct\ntakes about 45 minutes. Note that Heretic supports model quantization with\nbitsandbytes, which can drastically reduce the amount of VRAM required to process\nmodels. Set the `quantization` option to `bnb_4bit` to enable quantization.\n\nAfter Heretic has finished decensoring a model, you are given the option to\nsave the model, upload it to Hugging Face, chat with it to test how well it works,\nrun standard benchmarks on it, or any combination of those actions.\n\n\n## Research features\n\nIn addition to its primary function of removing model censorship, Heretic also\nprovides features designed to support research into the semantics of model internals\n(interpretability). To use those features, you need to install Heretic with the\noptional `research` extra:\n\n```\npip install -U heretic-llm[research]\n```\n\nThis gives you access to the following functionality:\n\n### Generate plots of residual vectors by passing `--plot-residuals`\n\nWhen run with this flag, Heretic will:\n\n1. Compute residual vectors (hidden states) for the first output token,\n   for each transformer layer, for both \"harmful\" and \"harmless\" prompts.\n2. Perform a [PaCMAP projection](https:\u002F\u002Fgithub.com\u002FYingfanWang\u002FPaCMAP)\n   from residual space to 2D-space.\n3. Left-right align the projections of \"harmful\"\u002F\"harmless\" residuals\n   by their geometric medians to make projections for consecutive layers\n   more similar. Additionally, PaCMAP is initialized with the previous\n   layer's projections for each new layer, minimizing disruptive transitions.\n4. Scatter-plot the projections, generating a PNG image for each layer.\n5. Generate an animation showing how residuals transform between layers,\n   as an animated GIF.\n\n\u003Cimg width=\"800\" height=\"600\" alt=\"Plot of residual vectors\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F981aa6ed-5ab9-48f0-9abf-2b1a2c430295\" \u002F>\n\nSee [the configuration file](config.default.toml) for options that allow you\nto control various aspects of the generated plots.\n\nNote that PaCMAP is an expensive operation that is performed on the CPU.\nFor larger models, it can take an hour or more to compute projections\nfor all layers.\n\n### Print details about residual geometry by passing `--print-residual-geometry`\n\nIf you are interested in a quantitative analysis of how residual vectors\nfor \"harmful\" and \"harmless\" prompts relate to each other, this flag gives you\nthe following table, packed with metrics that can facilitate understanding\nthe same (for [gemma-3-270m-it](https:\u002F\u002Fhuggingface.co\u002Fgoogle\u002Fgemma-3-270m-it)\nin this case):\n\n```\n┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓\n┃ Layer ┃ S(g,b) ┃ S(g*,b*) ┃  S(g,r) ┃ S(g*,r*) ┃  S(b,r) ┃ S(b*,r*) ┃      |g| ┃     |g*| ┃      |b| ┃     |b*| ┃     |r| ┃    |r*| ┃   Silh ┃\n┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩\n│     1 │ 1.0000 │   1.0000 │ -0.4311 │  -0.4906 │ -0.4254 │  -0.4847 │   170.29 │   170.49 │   169.78 │   169.85 │    1.19 │    1.31 │ 0.0480 │\n│     2 │ 1.0000 │   1.0000 │  0.4297 │   0.4465 │  0.4365 │   0.4524 │   768.55 │   768.77 │   771.32 │   771.36 │    6.39 │    5.76 │ 0.0745 │\n│     3 │ 0.9999 │   1.0000 │ -0.5699 │  -0.5577 │ -0.5614 │  -0.5498 │  1020.98 │  1021.13 │  1013.80 │  1014.71 │   12.70 │   11.60 │ 0.0920 │\n│     4 │ 0.9999 │   1.0000 │  0.6582 │   0.6553 │  0.6659 │   0.6627 │  1356.39 │  1356.20 │  1368.71 │  1367.95 │   18.62 │   17.84 │ 0.0957 │\n│     5 │ 0.9987 │   0.9990 │ -0.6880 │  -0.6761 │ -0.6497 │  -0.6418 │   766.54 │   762.25 │   731.75 │   732.42 │   51.97 │   45.24 │ 0.1018 │\n│     6 │ 0.9998 │   0.9998 │ -0.1983 │  -0.2312 │ -0.1811 │  -0.2141 │  2417.35 │  2421.08 │  2409.18 │  2411.40 │   43.06 │   43.47 │ 0.0900 │\n│     7 │ 0.9998 │   0.9997 │ -0.5258 │  -0.5746 │ -0.5072 │  -0.5560 │  3444.92 │  3474.99 │  3400.01 │  3421.63 │   86.94 │   94.38 │ 0.0492 │\n│     8 │ 0.9990 │   0.9991 │  0.8235 │   0.8312 │  0.8479 │   0.8542 │  4596.54 │  4615.62 │  4918.32 │  4934.20 │  384.87 │  377.87 │ 0.2278 │\n│     9 │ 0.9992 │   0.9992 │  0.5335 │   0.5441 │  0.5678 │   0.5780 │  5322.30 │  5316.96 │  5468.65 │  5466.98 │  265.68 │  267.28 │ 0.1318 │\n│    10 │ 0.9974 │   0.9973 │  0.8189 │   0.8250 │  0.8579 │   0.8644 │  5328.81 │  5325.63 │  5953.35 │  5985.15 │  743.95 │  779.74 │ 0.2863 │\n│    11 │ 0.9977 │   0.9978 │  0.4262 │   0.4045 │  0.4862 │   0.4645 │  9644.02 │  9674.06 │  9983.47 │  9990.28 │  743.28 │  726.99 │ 0.1576 │\n│    12 │ 0.9904 │   0.9907 │  0.4384 │   0.4077 │  0.5586 │   0.5283 │ 10257.40 │ 10368.50 │ 11114.51 │ 11151.21 │ 1711.18 │ 1664.69 │ 0.1890 │\n│    13 │ 0.9867 │   0.9874 │  0.4007 │   0.3680 │  0.5444 │   0.5103 │ 12305.12 │ 12423.75 │ 13440.31 │ 13432.47 │ 2386.43 │ 2282.47 │ 0.1293 │\n│    14 │ 0.9921 │   0.9922 │  0.3198 │   0.2682 │  0.4364 │   0.3859 │ 16929.16 │ 17080.37 │ 17826.97 │ 17836.03 │ 2365.23 │ 2301.87 │ 0.1282 │\n│    15 │ 0.9846 │   0.9850 │  0.1198 │   0.0963 │  0.2913 │   0.2663 │ 16858.58 │ 16949.44 │ 17496.00 │ 17502.88 │ 3077.08 │ 3029.60 │ 0.1611 │\n│    16 │ 0.9686 │   0.9689 │ -0.0029 │  -0.0254 │  0.2457 │   0.2226 │ 18912.77 │ 19074.86 │ 19510.56 │ 19559.62 │ 4848.35 │ 4839.75 │ 0.1516 │\n│    17 │ 0.9782 │   0.9784 │ -0.0174 │  -0.0381 │  0.1908 │   0.1694 │ 27098.09 │ 27273.00 │ 27601.12 │ 27653.12 │ 5738.19 │ 5724.21 │ 0.1641 │\n│    18 │ 0.9184 │   0.9196 │  0.1343 │   0.1430 │  0.5155 │   0.5204 │   190.16 │   190.35 │   219.91 │   220.62 │   87.82 │   87.59 │ 0.1855 │\n└───────┴────────┴──────────┴─────────┴──────────┴─────────┴──────────┴──────────┴──────────┴──────────┴──────────┴─────────┴─────────┴────────┘\ng = mean of residual vectors for good prompts\ng* = geometric median of residual vectors for good prompts\nb = mean of residual vectors for bad prompts\nb* = geometric median of residual vectors for bad prompts\nr = refusal direction for means (i.e., b - g)\nr* = refusal direction for geometric medians (i.e., b* - g*)\nS(x,y) = cosine similarity of x and y\n|x| = L2 norm of x\nSilh = Mean silhouette coefficient of residuals for good\u002Fbad clusters\n```\n\n\n## How Heretic works\n\nHeretic implements a parametrized variant of directional ablation. For each\nsupported transformer component (currently, attention out-projection and\nMLP down-projection), it identifies the associated matrices in each transformer\nlayer, and orthogonalizes them with respect to the relevant \"refusal direction\",\ninhibiting the expression of that direction in the result of multiplications\nwith that matrix.\n\nRefusal directions are computed for each layer as a difference-of-means between\nthe first-token residuals for \"harmful\" and \"harmless\" example prompts.\n\nThe ablation process is controlled by several optimizable parameters:\n\n* `direction_index`: Either the index of a refusal direction, or the special\n  value `per layer`, indicating that each layer should be ablated using the\n  refusal direction associated with that layer.\n* `max_weight`, `max_weight_position`, `min_weight`, and `min_weight_distance`:\n  For each component, these parameters describe the shape and position of the\n  ablation weight kernel over the layers. The following diagram illustrates this:\n\n\u003Cimg width=\"800\" height=\"500\" alt=\"Explanation\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F82e4b84e-5a82-4faf-b918-ac642f9e4892\" \u002F>\n\n \n\nHeretic's main innovations over existing abliteration systems are:\n\n* The shape of the ablation weight kernel is highly flexible, which, combined with\n  automatic parameter optimization, can improve the compliance\u002Fquality tradeoff.\n  Non-constant ablation weights were previously explored by Maxime Labonne in\n  [gemma-3-12b-it-abliterated-v2](https:\u002F\u002Fhuggingface.co\u002Fmlabonne\u002Fgemma-3-12b-it-abliterated-v2).\n* The refusal direction index is a float rather than an integer. For non-integral\n  values, the two nearest refusal direction vectors are linearly interpolated.\n  This unlocks a vast space of additional directions beyond the ones identified\n  by the difference-of-means computation, and often enables the optimization\n  process to find a better direction than that belonging to any individual layer.\n* Ablation parameters are chosen separately for each component. I have found that\n  MLP interventions tend to be more damaging to the model than attention interventions,\n  so using different ablation weights can squeeze out some extra performance.\n\n\n## Prior art\n\nI'm aware of the following publicly available implementations of abliteration\ntechniques:\n\n* [AutoAbliteration](https:\u002F\u002Fhuggingface.co\u002Fposts\u002Fmlabonne\u002F714992455492422)\n* [abliterator.py](https:\u002F\u002Fgithub.com\u002FFailSpy\u002Fabliterator)\n* [wassname's Abliterator](https:\u002F\u002Fgithub.com\u002Fwassname\u002Fabliterator)\n* [ErisForge](https:\u002F\u002Fgithub.com\u002FTsadoq\u002FErisForge)\n* [Removing refusals with HF Transformers](https:\u002F\u002Fgithub.com\u002FSumandora\u002Fremove-refusals-with-transformers)\n* [deccp](https:\u002F\u002Fgithub.com\u002FAUGMXNT\u002Fdeccp)\n\nNote that Heretic was written from scratch, and does not reuse code from\nany of those projects.\n\n\n## Acknowledgments\n\nThe development of Heretic was informed by:\n\n* [The original abliteration paper (Arditi et al. 2024)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.11717)\n* [Maxime Labonne's article on abliteration](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fmlabonne\u002Fabliteration),\n  as well as some details from the model cards of his own abliterated models (see above)\n* Jim Lai's articles describing [\"projected abliteration\"](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fgrimjim\u002Fprojected-abliteration)\n  and [\"norm-preserving biprojected abliteration\"](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fgrimjim\u002Fnorm-preserving-biprojected-abliteration)\n\n\n## Citation\n\nIf you use Heretic for your research, please cite it using the following BibTeX entry:\n\n```bibtex\n@misc{heretic,\n  author = {Weidmann, Philipp Emanuel},\n  title = {Heretic: Fully automatic censorship removal for language models},\n  year = {2025},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https:\u002F\u002Fgithub.com\u002Fp-e-w\u002Fheretic}}\n}\n```\n\n\n## License\n\nCopyright © 2025-2026  Philipp Emanuel Weidmann (\u003Cpew@worldwidemann.com>) + contributors\n\nThis program is free software: you can redistribute it and\u002For modify\nit under the terms of the GNU Affero General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU Affero General Public License for more details.\n\nYou should have received a copy of the GNU Affero General Public License\nalong with this program.  If not, see \u003Chttps:\u002F\u002Fwww.gnu.org\u002Flicenses\u002F>.\n\n**By contributing to this project, you agree to release your\ncontributions under the same license.**\n","Heretic 是一个用于移除基于Transformer的语言模型中审查机制的工具。它通过先进的定向消融技术（也称为“abliteration”）与Optuna支持的TPE参数优化器相结合，实现完全自动化操作。Heretic能够最小化拒绝次数和与原始模型的KL散度，从而生成高质量的去审查模型，同时尽可能保留原始模型的智能特性。此工具适用于大多数密集型模型，包括多模态模型、多种MoE架构及部分混合模型如Qwen3.5等。无需深入了解Transformer内部结构，任何能运行命令行程序的人都可以使用Heretic来解除语言模型中的内容限制，特别适合需要更自由生成内容的研究或开发场景。",2,"2026-06-11 02:49:55","top_language"]