Instructions to use Maorb23/Qwen3-wiki-mlop1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maorb23/Qwen3-wiki-mlop1 with Transformers:

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

pipe = pipeline("text-generation", model="Maorb23/Qwen3-wiki-mlop1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Maorb23/Qwen3-wiki-mlop1")
model = AutoModelForMultimodalLM.from_pretrained("Maorb23/Qwen3-wiki-mlop1")
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 Maorb23/Qwen3-wiki-mlop1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maorb23/Qwen3-wiki-mlop1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maorb23/Qwen3-wiki-mlop1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maorb23/Qwen3-wiki-mlop1

SGLang

How to use Maorb23/Qwen3-wiki-mlop1 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 "Maorb23/Qwen3-wiki-mlop1" \
    --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": "Maorb23/Qwen3-wiki-mlop1",
		"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 "Maorb23/Qwen3-wiki-mlop1" \
        --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": "Maorb23/Qwen3-wiki-mlop1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maorb23/Qwen3-wiki-mlop1 with Docker Model Runner:
```
docker model run hf.co/Maorb23/Qwen3-wiki-mlop1
```

Qwen3-wiki-mlop1

This model is a fine-tuned version of Qwen/Qwen3-4B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0847

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
2.3101	0.0561	100	2.2646
2.2156	0.1122	200	2.1251
2.0129	0.1683	300	2.0902
2.0186	0.2243	400	2.0847
2.1216	0.2804	500	2.0847

Framework versions

Transformers 5.9.0
Pytorch 2.10.0a0+b4e4ee81d3.nv25.12
Datasets 4.8.5
Tokenizers 0.22.1

Downloads last month: 17

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Maorb23/Qwen3-wiki-mlop1

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

(706)

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