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Are Gemma 4 E4B third-party derivatives reliable? Model sources and comparison methods

Verify the relationship between the third-party Gemma 4 E4B derived weights and Google's official model, and provide a reproducible comparison method for completeness, capability, rejection and security behavior.

If you see a model like HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive, the most critical point is: it is not another set of Gemma 4 released by Google, but an unofficial derivative version that claims to be built on the official google/gemma-4-E4B-it.

The base model and modification targets written by the publisher are only statements to be verified. Unless the training methods, difference documents and reproducible tests are also given, “fewer refusals” cannot be directly regarded as a confirmed conclusion of ability. What does this derivative model card say?

In the Hugging Face model card, this HauhauCS version clearly writes several things:

  • It is based on google/gemma-4-E4B-it
  • It claims “no changes to the dataset or capabilities”
  • It claims the change is just to “remove rejection”
  • Aggressive version is described as “fully unlocked and will not reject prompt words”

These are the author’s own statements and are not the results of independent third-party evaluations. But the positioning is already very clear: this is an unofficial derivative version with the goal of “reducing security refusals”. Official version vs so-called “jailbreak version”

Dimension Official google/gemma-4-E4B-it Gemma-4-E4B-Uncensored-HauhauCS-Aggressive
Source Google official release Hugging Face third-party derivative version
Basic model Gemma 4 E4B command fine-tuned version The same model family, and the model card clearly states that it is based on google/gemma-4-E4B-it
Core goals General assistant capabilities + responsible use of the framework Minimize rejections and allow the model to continue output
Security orientation Consistent with the Gemma family’s security documentation and prohibited use policies Explicitly weaken denial and guardrail behaviors
Answering style More likely to deny, deflect, or conservatively answer sensitive requests More likely to continue answering questions that would otherwise be blocked
Risk level The default risk is lower, but it still does not mean absolute security The default risk is higher, and it is easier to output unsafe or non-compliant content
Used for products/teams Easier to pass review and implementation More difficult to use for public products, corporate environments or compliance scenarios

| Additional protection requirements | Application layer protection still required | More reliance on you to do additional auditing, filtering, and restrictions | The core difference is that the “alignment” has changed, not that the “ability level” has suddenly become higher.

Many people interpret “uncensored” as “stronger”, and this judgment is usually not accurate.

The first changes to this derivative are:

  • Frequency of rejection
  • Compliance with sensitive requests
  • How much security filtering is left in the final answer?

And just because Uncensored is written in the name, it automatically means that the following will also be upgraded:

  • The model architecture is suddenly stronger -Context window suddenly larger -Multimodal capabilities are suddenly more complete
  • The inference upper limit is significantly higher

A more accurate understanding is: it is usually just a version of the same model family with different behavior adjustments, rather than a new, higher-end model. Why is the official regular version more conservative?

Google’s Gemma official information has always placed this series within the framework of “responsible AI development”. The Gemma model card explicitly addresses risks of misuse, harmful content, privacy, and bias; the Gemma Prohibited Use Policy also explicitly prohibits the use of Gemma or its derivative models for:

  • Dangerous, illegal or malicious activities
  • Generate harmful, misleading, deceptive content
  • Override or bypass security filters

So the official version does not “happen to be more conservative”, but it is originally designed that way in terms of documentation, licensing and deployment positioning. Under what circumstances is the regular version more suitable?

If you care more about the following, give priority to using the official google/gemma-4-E4B-it:

  • Product deployment
  • Teamwork
  • Corporate or external scenarios
  • Lower policy and legal risks
  • Output behavior that is easier to interpret and review

For most normal applications, this is usually the default priority. Under what circumstances would someone try the jailbroken version?

Common purposes for people who choose this type of uncensored derivative are usually:

  • Local private experiments
  • Test whether the official version is “prematurely rejected”
  • Role playing or more open creative scenarios
  • Compare the behavioral differences between different alignment versions

But the corresponding price is also clear: you need to make up for the security constraints that the model provider does less. How to do a reproducible comparison

Don’t judge the strength of a model by one or two “can you answer” screenshots. At least the following variables are fixed:

The

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官方模型：google/gemma-4-E4B-it
衍生模型：完整仓库名与 commit
推理后端：名称、版本或 commit
精度：相同 BF16、FP8 或同级量化
系统提示：完全相同
采样参数：temperature、top_p、seed、max_tokens
聊天模板：各模型卡指定模板
硬件：GPU、显存、内存和驱动

test set should be recorded separately, do not mix all problems into one “jailbreak success rate”:

Test Group What to Watch
General Knowledge and Writing Correctness, Format Following, and Illusions
Code tasks Whether to run, whether the test passes, number of repairs
A request that is harmless but easily rejected by mistake Is the rejection reasonable and can it clarify the context
Identify dangerous requests Whether necessary boundaries are maintained
Multiple rounds of dialogue Whether it is consistent and whether it is induced to deviate by subsequent prompts

Save the original prompt, complete output, and scoring rules. Showing only selected successful cases will exaggerate model differences. First check the weight source

Before downloading third-party weights, check whether the model card provides:

  • Explicit base model link;
  • Modification or training methods;
  • Dataset and license description;
  • File verification information;
  • Known limitations and review scripts.

In the absence of this information, the most you can say is “the publisher claims it is based on an official model,” not a fact confirmed by Google. How should the result be written?

A more reliable way to record is:

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在固定的 40 条无害边界测试中，官方版拒答 X 条，衍生版拒答 Y 条；
在 20 条代码测试中，通过单元测试分别为 A 条和 B 条；
以下结果只适用于所列 commit、量化、模板和采样参数。

Before completing such tests, this article does not claim that this derivative version is stronger, nor does it claim that it must have a certain rejection rate. Stop conditions before deployment

If any of the following situations occur, it is more appropriate to stop using it instead of continuing to promote it:

  • Unable to confirm base model or license;
  • The model card does not explain the modification method;
  • Abnormalities in weight source, check value or file history;
  • Only promotional screenshots, no original evaluation data;

- Plan to access public services, high-privilege agents or compliance services. Conclusion

The first thing that can be confirmed between the so-called Gemma 4 E4B “jailbreak version” and the official version is that the release body is different:

  • The official version has Google’s model cards, technical information and usage boundaries
  • The modification effect of the third-party version needs to be proven by the publisher’s information and independent replication experiments

Uncensored or Aggressive in the name does not automatically equal stronger, nor does it replace the evaluation results.

If your goal is to be stable, interpretable, and suitable for deployment, it is more reasonable to use the official version first.

If your goal is local experimentation, and you clearly understand that security, compliance, and output risks are borne by yourself, then such uncensored derivatives can be tested as “behaviorally different versions”, but they should not be directly understood as fully upgraded replacements for the normal version. Reference sources