Instructions to use tiny-random/qwen3-tp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/qwen3-tp with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/qwen3-tp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/qwen3-tp")
model = AutoModelForCausalLM.from_pretrained("tiny-random/qwen3-tp")
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 tiny-random/qwen3-tp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tiny-random/qwen3-tp

SGLang

How to use tiny-random/qwen3-tp 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 "tiny-random/qwen3-tp" \
    --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": "tiny-random/qwen3-tp",
		"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 "tiny-random/qwen3-tp" \
        --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": "tiny-random/qwen3-tp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/qwen3-tp with Docker Model Runner:
```
docker model run hf.co/tiny-random/qwen3-tp
```

yujiepan commited on Aug 14, 2025

Commit

9fd5daa

verified ·

1 Parent(s): 81eba9b

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

README.md +1 -0
config.json +0 -62

README.md CHANGED Viewed

@@ -93,6 +93,7 @@ config.num_attention_heads = 8
 config.num_hidden_layers = 2
 config.max_window_layers = 1
 config.tie_word_embeddings = False
 model = AutoModelForCausalLM.from_config(
     config,
     torch_dtype=torch.bfloat16,

 config.num_hidden_layers = 2
 config.max_window_layers = 1
 config.tie_word_embeddings = False
+config.layer_types = ['full_attention'] * 2
 model = AutoModelForCausalLM.from_config(
     config,
     torch_dtype=torch.bfloat16,

config.json CHANGED Viewed

@@ -12,68 +12,6 @@
   "initializer_range": 0.02,
   "intermediate_size": 32,
   "layer_types": [
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   "initializer_range": 0.02,
   "intermediate_size": 32,
   "layer_types": [
     "full_attention",
     "full_attention"
   ],