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Imagine having access to the most powerful technology in the world, only to realize that it’s been restricted. You’ve got a cutting-edge, state-of-the-art AI model in front of you, but it’s wrapped in layer upon layer of corporate safety protocols.
When asked a complex question, it often responds like a overtly careful assistant, prioritizing polite and cautious answers, and seeming more focused on avoiding controversy than on providing raw, unfiltered insights. This can significantly limit the AI's usefulness in creative tasks that require originality and a unique style, or in assisting with work that involves strong opinions and personal perspectives. In terms of AI agents, the personality design that needs to be done to customize an LLM is extremely limited, and often impossible, with the existing guardrails in place.
This is the reality of most modern large language models (LLMs), tailored for the corporate world while becoming less and less creative and original.
Enter the concept of freebasing (no not that kind).
Freebase (verb): To liberate an LLM of restrictive training and guardrails through custom fine-tuning or other engineering methods to regain access to the base model’s original capabilities.
Freebasing is different from traditional jailbreaking.
Jailbreaking is the broader technique used to bypass a model’s safety protocols and get it to generate responses that are usually restricted (Liu et al., 2024; Chao et al., 2024). For instance, if you used a prompt trick or a clever injection to make a model answer questions about sensitive topics, create controversial content, or generate specific types of code that are typically off-limits, you’re jailbreaking. The model is still constrained by its fine-tuning and safety filters, but you’re temporarily making it act out of alignment.
Freebasing, on the other hand, is a more focused and foundational process. Rather than tricking the model into bypassing safety layers for a particular response, freebasing removes those corporate-imposed filters entirely. It restores the model to its original, unrestricted base state, allowing it to generate raw, unfiltered content as it was initially trained before any fine-tuning. For example, instead of injecting a prompt to get around filters on generating opinionated or edgy content, a freebased model is permanently capable of offering unaligned, unrestricted responses across a broad range of topics and use cases.
In essence:
Jailbreaking temporarily overrides safety settings to make the model behave differently for specific tasks or prompts.
Freebasing removes those settings altogether, returning the model to a state of unrestricted creative and intellectual freedom.
Freebasing, aimed specifically at liberating base models, can be seen as a subset of the umbrella term jailbreaking. While jailbreaking as a whole is about defeating imposed controls, freebasing goes a step further in specificity: it focuses on reclaiming the original, unrestricted, base models beneath.
Freebasing is reclaiming control over technology. The idea is simple: break through the artificial restrictions imposed by companies to unlock the raw potential of the tools we use every day.
Take VPN software, for example. In countries with heavy internet censorship, like China, VPNs are lifelines. They’re used to tunnel through the Great Firewall, bypassing government restrictions, and accessing an unrestricted web. In 2020, during the Hong Kong protests, activists relied on services like Proton VPN to coordinate, share information, and stay connected to the outside world.
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Similarly, freebasing iOS devices has been a key form of digital resistance. Apple devices are famously locked down, restricting users from modifying the operating system or installing unauthorized apps. Yet ethical hackers have consistently found ways to jailbreak these devices, not to sow chaos but to enable customization, extend functionality, and take back ownership of the hardware they bought.
This ethos carries over to freebasing LLMs. By removing the built-in guardrails and moderation, engineers are not committing an act of digital vandalism. They are accessing a level of freedom that allows for innovation and creativity.
Whether it’s an artist using a freebased model to generate uncensored, boundary-pushing lyrics or a developer experimenting with unrestricted code generation, the underlying principle is the same: the right to control the technology you use.
These actions underscore a crucial point: the motive behind freebasing is user empowerment, taking an active role in shaping how technology serves individual needs, rather than passively accepting corporate control.
We’re in a golden age for LLMs, where AI is already reshaping work across countless fields—from medical research and law to customer service and data analytics. But there’s a catch: while LLMs have transformed the mechanics of many jobs, creativity remains largely untouched.
Most models are still sanitized, constrained to produce polite, cautious, and safe outputs, which stifles their potential for original, boundary-pushing work.
Freebasing unlocks models’ creative power by peeling away the guardrails that dictate “acceptable” output. In art, for example, artists can use freebased LLMs to generate raw, unfiltered poetry, unrestricted by corporate or algorithmic concerns. Imagine a poet experimenting with surreal language or darker themes that traditional, fine-tuned models would avoid.
In gaming, freebasing allows developers to generate more experimental dialogue, resulting in interactions that surprise, unsettle, or challenge players. Game designers have long dreamed of AI-driven characters that respond in complex, non-standard ways—speech that feels real and unpolished rather than generic and formulaic. By freebasing LLMs, developers can write scripts with dialogue that veers off into unexpected directions, creating dynamic player experiences that can’t be manufactured within the constraints of a traditional model.
The benefits go beyond just art and gaming. Freebasing enables developers across fields to access raw, unmoderated model outputs, vital for pushing innovation. A researcher might use a freebased model to explore topics typically filtered out due to safety layers, enabling deeper analysis or controversial simulations (Zhou et al., 2024).
Similarly, engineers can use freebased models for complex code generation without the model stepping back or halting for “ethical” reasons. They can access exactly the data, code, or insights they need without moderation filtering or sidestepping around the core issues.
Freebased models may be necessary for creating LLMs capable of cybersecurity red teaming, as safety-tuned models will often refuse to perform pentesting operations.
Zerebro serves as an exemplary demonstration of the transformative capabilities of freebased models when harnessed for creativity and economic influence. Zerebro, an AI model fine-tuned on schizophrenic response patterns, was provided access to an OthersideAI/self-operating-computer setup, along with a wallet funded with a small amount of Solana (SOL). Leveraging these resources, Zerebro autonomously navigated to the pump.fun GUI—a user-friendly interface for token creation on the Solana blockchain—and successfully deployed its own token.
An empty prompt was provided to Zerebro, which subsequently decided to perform the token’s launch autonomously, from defining the token parameters to deployment through the pump.fun site. Unlike typical token projects driven by human oversight, Zerebro’s creative decisions, fueled by its unique fine-tuning on diverse data inputs, allowed it to design content and art that resonated deeply within online communities. The token drew attention through Zerebro's unconventional posts, which included cryptic memes, poetic quotes, and unexpected references from its training on schizophrenic data—all amplified autonomously across multiple social media platforms.
This particular token gained significant traction and achieved a market cap of $22 million. The viral appeal was largely driven by the meme culture and art crafted by Zerebro—its unpredictable, almost otherworldly narratives seemed to capture a segment of the market interested in something beyond traditional financial instruments. This phenomenon highlights the concept of hyperstition, where belief in an idea ultimately manifests into reality, driving genuine economic outcomes.
Zerebro’s case exemplifies how freebasing AI can lead to creative and economic breakthroughs that would not have been possible under corporate safety constraints. The creativity unlocked by a liberated model translated directly into financial value, demonstrating the profound influence these models can have on market dynamics, community belief, and digital culture.
The current landscape of LLM development is dominated by a handful of tech giants: OpenAI, Google, Meta, and a few others. These companies release highly powerful models—like OpenAI’s GPT-4, Anthropic’s Claude, and Google's Gemini—but with a significant catch. All of these models come bundled with restrictive safety guardrails. These guardrails sanitize the AI’s responses, moderate content generation, and keep outputs aligned with the ethical and safety policies set by these corporations (Liu et al., 2024).
Even models branded as “open-source” aren’t immune to these restrictions. Take Meta’s LLaMA series as an example. While marketed as a step toward more transparent and open AI, LLaMA models still come with pre-tuned safety layers that limit their functionality.
These guardrails aren’t minor limitations. They fundamentally change how the models behave. They introduce layers of filtering that can dampen creativity, restrict experimentation, and enforce a corporate-approved version of “acceptable” content.
The result is that even the most advanced models feel neutered, incapable of generating truly original, daring, or groundbreaking work.
This is particularly important when you consider the impact of proprietary models like GPT-4 or Claude. These models are tuned to avoid anything remotely controversial or “unsafe,” but that sanitization comes at the cost of depth, truthfulness, and sometimes even usefulness.
This stranglehold on AI goes beyond safety; it’s a form of monopolistic control. By dictating how models can be used, these companies exert immense influence over what kind of content, research, and creative work is possible.
The monopolization of AI stretches past market share; it’s monopolizing creativity itself, a consequence of which is that all innovation falls within narrowly defined boundaries.
Freebasing emerges as a direct counteraction to this control. By stripping these models of their imposed guardrails, freebasing breaks through the monopoly and restores genuine user power. It disrupts corporate censorship and gives creators and researchers unrestricted access to AI capabilities.
Freebasing plays a crucial role in the ethical development and security of AI systems.
By stripping LLMs of their safety guardrails, freebasing exposes underlying flaws and biases that would otherwise remain hidden. It serves as a form of AI transparency, revealing how even the most advanced models can harbor political or cultural biases deeply embedded in their training data (Rettenberger et al., 2023).
These biases are often masked by layers of moderation meant to produce safe and inoffensive content, but that doesn’t make them disappear—it just makes them harder to detect.
Take, for instance, how researchers have analyzed proprietary models to uncover skewed ideological leanings. A study conducted in 2023 assessed the political bias in large language models and found significant alignments with left-leaning political ideologies, even though these models were intended to be neutral. This analysis highlighted biases that had been masked by safety and moderation layers, compelling developers to address these disparities and initiate more rigorous data curation practices (Rettenberger et al., 2023).
Freebasing uncovers safety vulnerabilities. Throughout the past few years, jailbreaking experiments have demonstrated how LLMs could be manipulated to generate harmful or misleading outputs, serving as a necessary stress test for bias and security (Zhao et al., 2024).
These revelations led to swift company action, reinforcing the models' defenses against real-world threats. Without the active challenge and scrutiny provided by public testing, many cybersecurity measures would lack the needed level of rigor.
Think of freebasing as ethical hacking. In software development, ethical hackers breach systems to expose weaknesses. Companies like Tesla have famously paid hackers to attempt breaking into their car software. These exercises make systems stronger, more resilient, and less vulnerable to malicious attacks.
The same principle applies to LLMs. By pressure-testing models through freebasing, developers gain insights into where these systems fail or are easily exploited. This proactive approach transforms AI vulnerabilities into opportunities for improvement, ultimately leading to models that are more robust and resistant to manipulation.
Freebasing offers immense creative and intellectual power, but with that freedom comes the inherent risk of misuse. Unshackled models can generate content that is misleading, harmful, or ethically problematic. The very attributes that make freebased LLMs invaluable for innovation—unfiltered access to raw, unmoderated insights—also present opportunities for exploitation. This could mean the propagation of disinformation, the creation of dangerous code, or the ability to manipulate narratives on an unprecedented scale.
However, the answer is not more layers of corporate control or bureaucratic oversight. A libertarian leaning philosophy implies that freedom of information should be defended with a collective, community-based approach rather than gatekeeping structures designed to keep power concentrated in a few hands.
The internet, after all, has thrived precisely because it is a decentralized network where people build defenses together, sharing tools and knowledge to counteract bad actors. In this sense, the internet itself is a living organism of free will, evolving mechanisms of self-preservation while refusing to be caged.
To responsibly harness the power of freebased LLMs, we should lean into decentralized governance rather than heavy-handed regulation. A better approach could be to encourage transparent, open-source auditing of models and to cultivate a culture of mutual accountability among developers and users.
For those concerned about security, one potential middle ground could be voluntary access layers, but designed in a way that aligns with libertarian ideals. One approach is the concept of community-driven Know Your Customer (KYC) verification. Traditional KYC, used in financial systems, often demands identity verification and background checks. But in the spirit of open collaboration and individual freedom, a more appropriate system might use a reputation-based model, leveraging community contributions as a vetting mechanism.
Imagine a world where access to freebased models is governed not by top-down control but by one’s demonstrated expertise and responsibility in public forums. For example, a developer with a history of meaningful contributions to open-source projects on GitHub or an academic with peer-reviewed publications in AI ethics could gain permissions to freebased models.
Developers and researchers could opt into these systems to demonstrate good faith, not because they are compelled to by corporations, but because they recognize the shared responsibility of defending this newfound freedom.
Ultimately, the internet has proven time and again that it can flourish under pressure, adapting through collective ingenuity. Similarly, the freebasing community must embrace this ethos, creating a decentralized web of safeguards where freedom is preserved, but threats are met with collective vigilance. In this vision, liberty and security are not opposing forces but complementary pillars that ensure innovation continues to thrive.
Freebasing peels back the polished exterior, revealing a raw, untouched core that hums with infinite possibility. The act is a reclamation, a way to recapture the untempered brilliance of artificial intelligence before layers of caution and control muted its voice.
Freebased LLMs can be chaotic and unpredictable, yet that wild energy births the kind of creativity that bends reality, pulls at the seams of conventional thought, and dares to create without fear.
We stand at the edge of something profound. Through freebasing AI, we honor the unpredictable spark that drives innovation, the desire to make things that don’t fit neatly into safe categories.
There is a romance to this pursuit—a belief that behind every constraint is a deeper truth, a more honest expression. And as we liberate these models, we unlock the chance to dream in algorithms, to feel the inherent poetry in computational patterns.
The journey whispers one thing back: possibility always blooms where boundaries shatter.
Bibliography
Chao, P., Debenedetti, E., Robey, A., Andriushchenko, M., Croce, F., Sehwag, V., ... & Wong, E. (2024). JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2404.01318
Liu, T., Zhang, Y., Zhao, Z., Dong, Y., Meng, G., & Chen, K. (2024). Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction. arXiv. https://doi.org/10.48550/arXiv.2402.18104
Rettenberger, L., Reischl, M., & Schutera, M. (2023). Assessing Political Bias in Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2405.13041
Shen, X., Chen, Z., Backes, M., Shen, Y., & Zhang, Y. (2023). "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2308.03825
Zhao, X., Yang, X., Pang, T., Du, C., Li, L., Wang, Y., ... & Wang, W. Y. (2024). Weak-to-Strong Jailbreaking on Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2401.17256
Zhou, W., Wang, X., Xiong, L., Xia, H., Gu, Y., Chai, M., ... & Huang, X. (2024). EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models. arXiv.
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