Instructions to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-2B-Base")
model = PeftModel.from_pretrained(base_model, "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b")

Transformers

How to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b with Transformers:

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

pipe = pipeline("text-generation", model="thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b

SGLang

How to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b 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 "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b" \
    --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": "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b",
		"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 "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b" \
        --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": "thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b with Docker Model Runner:
```
docker model run hf.co/thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b
```

Thai Gender Bias Span Extraction — Qwen 3.5 2B (LoRA)

LoRA adapter for unsloth/Qwen3.5-2B-Base that detects gender-biased spans in Thai social-media text. Given Thai input, the model returns the same text with bias spans wrapped in inline tags:

<GB-ATTACK>...</GB-ATTACK> — gender-based attacks, insults, dehumanization
<GB-NORMATIVE>...</GB-NORMATIVE> — stereotypes, gender-role policing
<GB-SEX>...</GB-SEX> — sexualized degradation tied to gender

Text without gender bias is returned unchanged.

Training

Fine-tuned with Unsloth on annotated + synthetic Thai examples: thunder121x/thai-gender-bias-training-data

Source code and pipeline: github.com/thunder121x/gender-bias-detection (see services/lora_finetuning/ for the training and inference scripts, including the exact system prompt required at inference time).

Sequence classifier: thunder121x/thai-gender-bias-classifier-minilm

Authors

Nanphat Tongsirisukool — nanphatx@hotmail.com
Natcha Trairattanasak — pnbookclub@gmail.com

Downloads last month: 2

Model tree for thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

unsloth/Qwen3.5-2B-Base

Adapter

(2)

this model

thunder121x
/

thai-gender-bias-span-extraction-qwen3.5-2b

Thai Gender Bias Span Extraction — Qwen 3.5 2B (LoRA)

Training

Related

Authors

Model tree for thunder121x/thai-gender-bias-span-extraction-qwen3.5-2b