Instructions to use SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m")
model = AutoModelForCausalLM.from_pretrained("SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m

SGLang

How to use SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m with Docker Model Runner:
```
docker model run hf.co/SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

这是一个基于 Qwen/Qwen3-0.6B-Base 进行指令微调的语言模型。

Step	Eval Loss	下降幅度 (Change)
2000	1.100951	0.000000
4000	1.065262	-0.035689
6000	1.044676	-0.020586
8000	1.030341	-0.014335
10000	1.020166	-0.010175
12000	1.012897	-0.007268
14000	1.006105	-0.006792
16000	1.002148	-0.003957
18000	0.996928	-0.005221
20000	0.992385	-0.004543
22000	0.989214	-0.003170
24000	0.986078	-0.003136
26000	0.982033	-0.004045
28000	0.979861	-0.002172
30000	0.976318	-0.003543
32000	0.974872	-0.001446
34000	0.972485	-0.002387
36000	0.970818	-0.001667
38000	0.968975	-0.001842
40000	0.966464	-0.002511
42000	0.963575	-0.002889
44000	0.963056	-0.000518
46000	0.960805	-0.002251
48000	0.957807	-0.002998

这是模型的第二个测试版本，提供以下三种功能：

自然语言转标签 (NL to Tag)
标签转自然语言 (Tag to NL)
标签补全 (Tag to Tag)

跟第一个版本的区别在于，这个使用了300万数据集，64dim，其余几乎相同。

相同测试集下，与第一个版本相比 eval_loss=1.02→0.957807

模型详情

基础模型: Qwen/Qwen3-0.6B-Base
微调方法: 指令微调 (Instruction Fine-tuning)
训练数据: 模型使用了约 300 万条数据进行训练。数据集包含三个指令任务，清洗筛选后每个任务约有 92 万条训练样本。

如何使用

请使用特定的指令 Token 来引导模型执行相应任务。输入和输出需要使用指定的 XML 格式包裹。

1. 自然语言描述 → Danbooru 标签 (<NLTOTAG>)

指令: <NLTOTAG>

输入:

<caption>This illustration features a young girl standing with a smile, looking directly at the viewer. She's wearing a white beret, and her long, light-colored hair is styled with pigtails, adorned with ribbons. She sports round glasses and a white collared shirt with long sleeves, layered with a light orange sweater vest. A pleated skirt that matches the ribbons, and thigh-highs complete her outfit. Her hands are clasped over her chest, possibly holding a book or other object. The background is plain white, emphasizing the character.</caption>

输出:

<tags><special>1girl</special><artists></artists><characters></characters><copyrights>original</copyrights><general>skirt, thighhighs, pleated_skirt, smile, white_background, hands_on_own_chest, looking_at_viewer, long_hair, glasses, solo, simple_background, long_sleeves, sketch</general><rating>safe</rating></tags>

2. Danbooru 标签 → 自然语言描述 (<TAGTONL>)

指令: <TAGTONL>

输入:

<tags><special>2girls</special><artists></artists><characters></characters><copyrights></copyrights><general>bracelet, casual, purple_hair, oekaki, outdoors, white_flower, blouse, jewelry, pendant, drill_hair, dandelion, closed_eyes, smile, short_sleeves, frills</general><rating>safe</rating></tags>

输出:

<caption>The image is a vibrant, anime-style illustration of two young women, likely teens, lying together on a bed of green grass dotted with dandelions. The composition features a close-up, overhead perspective. The first girl has brown drill hair styled in two high twintails, secured with pink bows featuring black polka dots, and she's dressed in a polka-dotted dress with frilly details and short sleeves, with a pink heart pendant necklace. She's smiling widely, eyes closed, showing a blush on her cheeks, and is looking at the other girl. The second girl, also wearing a similar style dress with a pink bow, and has pink hair, looks back at her with a smile, hugging her. Both girls have bracelets. The overall atmosphere is bright and cheerful, suggesting a moment of friendship and intimacy in a sunny outdoor setting.</caption>

3. 标签补全与优化 (<TAGTOTAG>)

指令: <TAGTOTAG>

输入:

<tags><special>2girls</special><artists></artists><characters></characters><copyrights></copyrights><general>looking_at_another, hug, camisole, on_back, blush, oekaki, field, jewelry, dandelion_clock, on_grass, pendant, blouse, short_sleeves, dandelion, frills, bow, casual, smile, sleeveless, outdoors, brown_hair, pink_bow, hair_ribbon, polka_dot, shirt, short_hair, yellow_flower, lying, flower, closed_eyes, bracelet, drill_hair, sparkle, grass, on_side, purple_hair, ribbon, on_ground, white_flower</general><rating>safe</rating></tags>

输出:

<tags><special>2girls</special><artists></artists><characters></characters><copyrights></copyrights><general>closed_eyes, hair_ribbon, oekaki, sleeveless, sparkle, hug, pink_bow, white_flower, short_hair, looking_at_another, dandelion_clock, ribbon, pendant, flower, lying, purple_hair, bracelet, smile, bow, brown_hair, frills, blush, jewelry, short_sleeves, on_grass, casual, grass, outdoors, shirt, blouse, field, yellow_flower, camisole, on_back, twintails, polka_dot, on_ground, on_side, dandelion</general><rating>safe</rating></tags>

已知问题

<TAGTOTAG> 的数据集构建。 可能把输入（需要补全的 tags）和输出（补全后的 tags）都随机 drop 掉了一部分。理论上只应该 drop 输入，这导致模型这边奇怪。
短样本筛选不力。 没有把特别短的 tag 样本过滤。

未来计划

重新处理数据集： 换成10% dan和full dan进行训练。
模型参数调优： dim拉高/换1.5b的qwen

引用

没有引用，孩子不懂事炼着玩的

Downloads last month: -

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for SakikoLab/Qwen3-0.6B-Prompt-Gen-beta-3m

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(595)

this model