Instructions to use yeonjooooni/Qwen3_8B_WEASEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yeonjooooni/Qwen3_8B_WEASEL with Transformers:

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

pipe = pipeline("text-generation", model="yeonjooooni/Qwen3_8B_WEASEL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yeonjooooni/Qwen3_8B_WEASEL")
model = AutoModelForCausalLM.from_pretrained("yeonjooooni/Qwen3_8B_WEASEL")
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 Settings

vLLM

How to use yeonjooooni/Qwen3_8B_WEASEL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yeonjooooni/Qwen3_8B_WEASEL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yeonjooooni/Qwen3_8B_WEASEL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yeonjooooni/Qwen3_8B_WEASEL

SGLang

How to use yeonjooooni/Qwen3_8B_WEASEL 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 "yeonjooooni/Qwen3_8B_WEASEL" \
    --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": "yeonjooooni/Qwen3_8B_WEASEL",
		"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 "yeonjooooni/Qwen3_8B_WEASEL" \
        --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": "yeonjooooni/Qwen3_8B_WEASEL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yeonjooooni/Qwen3_8B_WEASEL with Docker Model Runner:
```
docker model run hf.co/yeonjooooni/Qwen3_8B_WEASEL
```

Qwen3_8B_WEASEL

Qwen3_8B_WEASEL is a WEASEL fine-tuned variant of Qwen/Qwen3-8B for web-agent style reasoning and action generation.

Model Description

Base model: Qwen/Qwen3-8B
Format: Hugging Face Transformers + Safetensors
Intended domain: web navigation, browser interaction, reasoning-to-action generation

Intended Uses

This model is intended for:

Research on language agents for web environments
Multi-turn prompting with reasoning and action generation
Offline evaluation on browser-task datasets
Experiments involving tool-use and UI-grounded planning

Out-of-Scope Uses

This model is not intended for:

Production autonomy without action validation
Safety-critical or regulated domains
Situations requiring guaranteed factuality or correctness
Fully trusted execution of generated actions

Training Data

This model was fine-tuned on a WEASEL/AgentTrek-style web-agent dataset with message-based interaction trajectories. The underlying trajectories come from yeonjooooni/agenttrek-WEASEL, while the reasoning traces were newly generated using inference from Qwen3-8B.

Training Objective

The fine-tuning objective emphasizes:

following browser-task instructions
producing coherent next actions
grounding outputs in observed state
supporting reasoning-heavy interaction traces

Limitations

The model can generate invalid or non-executable actions
Reasoning traces may sound confident while being wrong
Performance may degrade outside the web-agent data distribution
Prompt wording can materially affect output quality
Additional runtime checks are recommended before action execution

Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "yeonjooooni/Qwen3_8B_WEASEL"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)