Which text llm are least restrictive and most uncensored as of today
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Executive summary
Public reporting in 2025–2026 shows a clear split: many community-maintained, locally runnable models are described as “uncensored” (commonly cited examples include Dolphin, WizardLM, Mixtral/Ghost variants, and LLaMA-family forks), while major cloud vendors and conferences push disclosure, safety and mixed policies that limit truly open, permissive deployments (cloud-hosted uncensored models are rare; one study found only 1 uncensored model among 529 API offerings) [1] [2] [3]. Researchers warn that removing guardrails exposes models to jailbreaks and reliability failures such as adversarial phrasing and poetic jailbreaks [4] [5].
1. The marketplace: many “uncensored” models, but mainly community-driven
A large portion of reporting and lists of “uncensored” LLMs come from community blogs and guides that highlight locally deployable models like Dolphin series, Mixtral/Dolphin variants, WizardLM, Guanaco and LLaMA-family forks marketed or configured to be permissive; vendors and toolmakers also package these for easy local use (Ollama, PrivateLLM etc.) [1] [6] [2] [7]. Academic and aggregator write-ups note that the term “uncensored” is commonly used to mean fewer built‑in refusal rules rather than a single standard of openness [8].
2. Where truly uncensored models live: local, open-source, and fine‑tuned forks
Most examples of low‑restriction models are local or open‑source forks and fine‑tunes that remove alignment layers (often via community “abliteration” or permissive fine‑tuning), and they are most often distributed on community hubs and through local runtimes (e.g., Ollama, Private LLM apps) [9] [7] [10]. Researchers tracking availability found cloud-hosted uncensored offerings are rare: an empirical study observed only a single uncensored model among 529 models in a major API marketplace [3].
3. Tradeoffs: quality, security and the “uncensoring” costs
Technical posts show that techniques used to “uncensor” models, such as abliteration (targeting and removing a “refusal direction”), can reduce safety but also sometimes degrade model quality; authors sometimes “heal” models afterwards with reinforcement approaches, indicating tradeoffs between permissiveness and fidelity [9]. Security reporting highlights real risks: flaws in integrations (e.g., Copilot plugin RCE) and jailbreak vectors (poetic formatting, adversarial prompts) make less‑guarded models attractive to attackers or misuse [11] [5].
4. Institutional reaction: disclosure and split policies at conferences and vendors
Top conferences and institutions are formalizing rules to manage LLM use: ICLR and ICML updates require disclosure of LLM use and in some cases offer both conservative and permissive policies for reviewers and authors, signaling an ecosystem that wants both innovation and accountability [12] [13]. These policies increase pressure on hosted vendors to enforce safety and on users to document when alignment has been disabled [12] [13].
5. What “least restrictive” actually means in practice
Across sources, “least restrictive” is a moving target: some community models are labeled uncensored because they obey prompts more readily (Dolphin, WizardLM, Mythomax, DeepSeek distills are repeatedly cited), but that does not mean they are identical — licensing, fine‑tuning history and deployment tooling differ and affect behavior and legal constraints [2] [14] [10]. Benchmarks and leaderboards exist, but open‑source guides warn that “uncensored” status is about configuration and distribution more than a model’s innate property [8] [15].
6. Risks and researcher warnings: reliability and exploitability
Academic and media reporting documents systematic failure modes: LLMs can latch on to surface patterns rather than understanding, making them vulnerable to adversarial prompts; poetic or specially structured text can reliably bypass some guardrails, enabling jailbreaking and harmful outputs [4] [5]. These findings mean a model that is “least restrictive” in output policies is also often more exploitable and less predictable in high‑stakes contexts [4] [5].
7. Practical advice for seekers of permissive models
If you need an uncensored model for research or local experimentation, community guides point to Dolphin, Mixtral/Mistral variants, WizardLM, Guanaco and LLaMA forks and recommend local runtimes like Ollama or PrivateLLM for self‑hosting [1] [6] [7]. Keep in mind: cloud APIs rarely offer genuinely uncensored models; check third‑party tracking studies and model repositories for current availability and licensing terms before deploying [3] [8].
Limitations: available sources are mostly blogs, community guides and a few empirical studies; authoritative vendor roadmaps and up‑to‑date cloud catalogs are not comprehensively included in the set of results provided (not found in current reporting).