What are the top uncensored AI models for academic research in 2024?

1. What people mean by “uncensored” and why it matters for research

“Uncensored” in the current reporting usually refers to models or forks where commercial guardrails, safety prompts or post-training filters have been reduced or removed so the model will answer a wider range of prompts; authors and reviewers note this is often achieved by post-training, LoRA-style fine-tunes or system-prompt tweaks ^{[5] [1]}. Proponents argue uncensored models let researchers probe model capability, bias, and failure modes without intermediary moderation; critics warn the same openness increases risk of generating harmful, illegal, or misleading outputs — a tension widely discussed in community blogs and industry write-ups ^{[6] [1]}.

2. The names that appear most often in lists and repos

Multiple non-academic roundups and collections repeatedly surface variants of “Dolphin” (Mixtral/Llama-based variants), Perplexity’s open releases (pplx-70b / R1-1776), and community-maintained Llama-family builds as commonly recommended uncensored models for exploratory use ^{[6] [2] [3]}. Hugging Face collections also host many roleplay/NSFW and uncensored models, demonstrating there is an accessible ecosystem of such weights and forks ^[7].

3. Where researchers actually get these models and how they run them

Practical routes cited in reporting include Hugging Face model hubs, local runtimes (Ollama), and small platforms that distribute post-trained weights or APIs; guides and blog posts emphasize Ollama and Hugging Face as common ways to deploy uncensored variants locally or privately ^{[8] [5] [2]}. Several outlets also highlight web platforms (FreedomGPT, Venice) that aggregate uncensored models or present uncensored “AI app stores” for users to test via a web UI ^{[9] [10]}.

4. Evidence of growth and the academic reaction

A preprint and industry coverage find a clear uptick in uncensored-model activity, with the most dramatic growth in late 2024 as techniques to strip or bypass safety became more accessible ^[1]. Academia responded by producing guidance for ethical AI use in research—Oxford and collaborators published formal guidelines on ethical use and acknowledgement of LLMs, reflecting heightened concern where uncensored outputs could affect research integrity ^[11].

5. Capabilities researchers cite as useful—and the risks attached

Enthusiasts claim uncensored variants can be superior for stress-testing reasoning, exploring forbidden-topic biases, or reproducing adversarial behaviours; articles promoting specific models stress coding performance and versatility as draws ^{[6] [2]}. Yet reviewers and preprints show these same models can be “dealigned” cheaply (e.g., via LoRA) and may produce unchecked harmful content or propagate bias, making them risky for unvetted public use ^{[1] [5]}.

6. A balanced checklist for academics considering uncensored models

Available reporting recommends: (a) use local or private deployments (Ollama/Hugging Face) to control data flow ^{[8] [7]}; (b) pair experiments with institutional ethics review and the new academic LLM guidelines where applicable ^[11]; (c) document use with an LLM acknowledgement template as promoted by Oxford collaborators ^[11]; and (d) avoid publishing or releasing model outputs that could enable harm — a limitation repeatedly emphasized in guides ^{[5] [1]}.

7. Bottom line: where the coverage leaves gaps

Coverage provides lists of popular uncensored models and deployment tips, and documents rapid growth and ethical concern through late 2024 and into 2025 ^{[2] [1] [3]}. What is not found in the current reporting is a peer‑reviewed, comprehensive ranking of “top” uncensored models explicitly tailored for academic reproducibility (available sources do not mention a single, authoritative, peer‑reviewed 2024 ranking for research use). Use the scattered community lists and platform pages to locate weights, but pair any experimentation with formal ethical oversight and transparent documentation ^{[7] [11]}.