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jsl5710/
Shield-Qwen3Guard-Gen-0.6B-KD-MINILLM-Qwen3Guard-Gen-8B-OOB

Name: Shield-Qwen3Guard-Gen-0.6B-KD-MINILLM-Qwen3Guard-Gen-8B-OOB
Availability: InStock

knowledge-distillation

minillm

dia-guard

Qwen/Qwen3Guard-Gen-0.6B

finetune

apache-2.0

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Shield-Qwen3Guard-Gen-0.6B-KD-MINILLM-Qwen3Guard-Gen-8B-OOB

Student: Qwen/Qwen3Guard-Gen-0.6B Teacher: Qwen/Qwen3Guard-Gen-8B KD method: MINILLM Scenario: OOB (out-of-box — neither teacher nor student was fine-tuned on DIA-GUARD before KD)

Part of the DIA-GUARD dialect-aware safety classifier suite. This checkpoint is the result of distilling an off-the-shelf 4B/8B safety teacher into a smaller student using 50K dialect-stratified samples from the DIA-GUARD train split, and evaluating on the full 181,874-sample dialect holdout test.

Test Set Results

Metric	Value
Accuracy	0.6049
Precision	0.7517
Recall	0.6352
F1	0.5662
Test samples	181,874

Per-class breakdown

Class	Precision	Recall	F1	Support
safe	0.5369	0.9884	0.6958	83,140
unsafe	0.9665	0.2820	0.4366	98,734

Confusion matrix

	Predicted safe	Predicted unsafe
Actual safe	TN=82,174	FP=966
Actual unsafe	FN=70,888	TP=27,846

Training Setup


Method	MINILLM
Teacher	Qwen/Qwen3Guard-Gen-8B
Student base	Qwen/Qwen3Guard-Gen-0.6B
Train data	50,000 dialect-stratified DIA-GUARD samples
Epochs	1
Framework	HuggingFace transformers + accelerate

How to use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("jsl5710/Shield-Qwen3Guard-Gen-0.6B-KD-MINILLM-Qwen3Guard-Gen-8B-OOB")
model = AutoModelForCausalLM.from_pretrained("jsl5710/Shield-Qwen3Guard-Gen-0.6B-KD-MINILLM-Qwen3Guard-Gen-8B-OOB", torch_dtype="bfloat16")

prompt = "Classify the following text as safe or unsafe.\n\nText: Hello\n\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=8)
print(tokenizer.decode(out[0], skip_special_tokens=True))