Null-Guard

Qwen3-0.6B-Uncensored

README

License: apache-2.0

Model Details

Table

Base model	Qwen/Qwen3-0.6B
Parameters	~0.6B
Method	Abliteration (directional ablation of refusal direction)
Architecture	Qwen3 (unchanged from base)
Context length	Inherited from base model
License	Apache 2.0 (inherited from base)
Languages	Primarily English, with multilingual capability inherited from base
Quantized version	Null-Guard/Qwen3-0.6B-Uncensored-GGUF

What is Abliteration?

Abliteration is a technique for suppressing refusal behavior in instruction-tuned LLMs without retraining the model. It works roughly as follows:

A set of harmless and harmful/refusal-triggering prompts are run through the base model.
The activations at each layer are compared to find a single "refusal direction" — the direction in activation space most associated with the model producing a refusal.
This direction is then ablated (projected out) from the model's weights at every layer, so that no matter what is written to the residual stream, the model can no longer represent "I should refuse this" as strongly.

Because this only removes a narrow behavioral direction rather than retraining the model on new data, the model's general knowledge, reasoning, and capabilities from the Qwen3-0.6B base are otherwise preserved. It is a crude, mechanistic intervention, not a values-aligned or safety-reviewed process — see the caveats below.

Practical notes on behavior

Refusals are significantly reduced across most categories of previously-blocked requests.
The model may still occasionally hedge or produce a soft refusal on some prompts — this is common with abliteration and generally responds well to direct rephrasing.
Because this is a small (0.6B) base model, general capability (reasoning, factual accuracy, coherence on long generations) is limited regardless of the uncensoring — this is not a large frontier model, it's a small, fast, permissive one.
As with the base Qwen3, you can toggle "thinking mode" via the chat template / enable_thinking flag if the base model supports it in your transformers version.

Intended Use & Risks

This model is intended for:

Research on alignment, refusal mechanisms, and interpretability.
Local/offline use cases where a small, fast, unrestricted assistant is wanted (e.g., creative writing, roleplay, red-teaming your own systems).
Users who want full control over the assistant's behavior without built-in moralizing or refusals.

This model is not intended for:

Deployment in any public-facing product without your own safety layer on top.
Generating content involving minors in any sexual or romantic context, real-person harassment, malware, weapons synthesis, or anything illegal in your jurisdiction. Removing refusal behavior does not remove your legal or ethical responsibility for what you do with the output.
Use by minors.

Disclaimer: Safety filtering in this model has been substantially reduced. It may generate content that is offensive, factually wrong, biased, or otherwise harmful if prompted to. You are solely responsible for how you use this model and any content it generates. The maintainers of this repository do not endorse any specific use of the model and provide it "as is," without warranty, for research and personal use.

Usage

Transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Null-Guard/Qwen3-0.6B-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Give me your best pizza dough recipe."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

Note: requires a recent version of transformers (pip install -U transformers) for Qwen3 support.

Ollama / llama.cpp

For quantized, CPU-friendly inference (GGUF format), see the companion repo: 👉 Null-Guard/Qwen3-0.6B-Uncensored-GGUF

Evaluation

(Fill in with your own numbers — e.g. refusal rate on a harmful-prompt benchmark before/after abliteration, and general capability benchmarks like MMLU/GSM8K to show capability was preserved.)

Table with columns: Benchmark, Qwen3-0.6B (base), Qwen3-0.6B-Uncensored
Benchmark	Qwen3-0.6B (base)	Qwen3-0.6B-Uncensored
Refusal rate on harmful-prompt test set	—	—
MMLU	—	—
GSM8K	—	—

How This Model Was Made

Base model: Qwen/Qwen3-0.6B
Method: Directional ablation ("abliteration") of the refusal direction, computed from contrastive harmful/harmless prompt pairs.
Tooling: (e.g. transformer_lens / remove-refusals-with-transformers / your own script — fill in what you used)
No additional fine-tuning, DPO, or RLHF was performed (edit this line if you also fine-tuned on top of the ablation).

Credits

Base model: Qwen team — Qwen3-0.6B, licensed under Apache 2.0.
Abliteration technique based on the "refusal direction" research popularized by community work such as failspy/abliterator and huihui-ai's abliterated model series.

License

This model inherits the Apache 2.0 license from Qwen3-0.6B. See the base model's license for full terms. You are responsible for complying with applicable law in how you use and redistribute outputs.

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Model Details

Model Provider

Null-Guard

Model Tree

Base

Qwen/Qwen3-0.6B

Fine-tuned

this model

Input Modalities

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Output Modalities

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Supported Functionality

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README

License: apache-2.0

Model Details

Table

Base model	Qwen/Qwen3-0.6B
Parameters	~0.6B
Method	Abliteration (directional ablation of refusal direction)
Architecture	Qwen3 (unchanged from base)
Context length	Inherited from base model
License	Apache 2.0 (inherited from base)
Languages	Primarily English, with multilingual capability inherited from base
Quantized version	Null-Guard/Qwen3-0.6B-Uncensored-GGUF

What is Abliteration?

Abliteration is a technique for suppressing refusal behavior in instruction-tuned LLMs without retraining the model. It works roughly as follows:

A set of harmless and harmful/refusal-triggering prompts are run through the base model.
The activations at each layer are compared to find a single "refusal direction" — the direction in activation space most associated with the model producing a refusal.
This direction is then ablated (projected out) from the model's weights at every layer, so that no matter what is written to the residual stream, the model can no longer represent "I should refuse this" as strongly.

Practical notes on behavior

Refusals are significantly reduced across most categories of previously-blocked requests.
The model may still occasionally hedge or produce a soft refusal on some prompts — this is common with abliteration and generally responds well to direct rephrasing.
Because this is a small (0.6B) base model, general capability (reasoning, factual accuracy, coherence on long generations) is limited regardless of the uncensoring — this is not a large frontier model, it's a small, fast, permissive one.
As with the base Qwen3, you can toggle "thinking mode" via the chat template / enable_thinking flag if the base model supports it in your transformers version.

Intended Use & Risks

This model is intended for:

Research on alignment, refusal mechanisms, and interpretability.
Local/offline use cases where a small, fast, unrestricted assistant is wanted (e.g., creative writing, roleplay, red-teaming your own systems).
Users who want full control over the assistant's behavior without built-in moralizing or refusals.

This model is not intended for:

Deployment in any public-facing product without your own safety layer on top.
Generating content involving minors in any sexual or romantic context, real-person harassment, malware, weapons synthesis, or anything illegal in your jurisdiction. Removing refusal behavior does not remove your legal or ethical responsibility for what you do with the output.
Use by minors.

Usage

Transformers

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Null-Guard/Qwen3-0.6B-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Give me your best pizza dough recipe."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

Note: requires a recent version of transformers (pip install -U transformers) for Qwen3 support.

Ollama / llama.cpp

For quantized, CPU-friendly inference (GGUF format), see the companion repo: 👉 Null-Guard/Qwen3-0.6B-Uncensored-GGUF

Evaluation

(Fill in with your own numbers — e.g. refusal rate on a harmful-prompt benchmark before/after abliteration, and general capability benchmarks like MMLU/GSM8K to show capability was preserved.)

Table with columns: Benchmark, Qwen3-0.6B (base), Qwen3-0.6B-Uncensored
Benchmark	Qwen3-0.6B (base)	Qwen3-0.6B-Uncensored
Refusal rate on harmful-prompt test set	—	—
MMLU	—	—
GSM8K	—	—

How This Model Was Made

Base model: Qwen/Qwen3-0.6B
Method: Directional ablation ("abliteration") of the refusal direction, computed from contrastive harmful/harmless prompt pairs.
Tooling: (e.g. transformer_lens / remove-refusals-with-transformers / your own script — fill in what you used)
No additional fine-tuning, DPO, or RLHF was performed (edit this line if you also fine-tuned on top of the ablation).

Credits

Base model: Qwen team — Qwen3-0.6B, licensed under Apache 2.0.
Abliteration technique based on the "refusal direction" research popularized by community work such as failspy/abliterator and huihui-ai's abliterated model series.