Instructions to use Minami-su/Qwen1.5-0.5B-Chat_llamafy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minami-su/Qwen1.5-0.5B-Chat_llamafy with Transformers:

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

pipe = pipeline("text-generation", model="Minami-su/Qwen1.5-0.5B-Chat_llamafy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_llamafy")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_llamafy", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Minami-su/Qwen1.5-0.5B-Chat_llamafy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minami-su/Qwen1.5-0.5B-Chat_llamafy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minami-su/Qwen1.5-0.5B-Chat_llamafy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Minami-su/Qwen1.5-0.5B-Chat_llamafy

SGLang

How to use Minami-su/Qwen1.5-0.5B-Chat_llamafy 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 "Minami-su/Qwen1.5-0.5B-Chat_llamafy" \
    --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": "Minami-su/Qwen1.5-0.5B-Chat_llamafy",
		"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 "Minami-su/Qwen1.5-0.5B-Chat_llamafy" \
        --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": "Minami-su/Qwen1.5-0.5B-Chat_llamafy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Minami-su/Qwen1.5-0.5B-Chat_llamafy with Docker Model Runner:
```
docker model run hf.co/Minami-su/Qwen1.5-0.5B-Chat_llamafy
```

Qwen1.5-0.5B-Chat_llamafy / pytorch_model.bin

Minami-su

Upload pytorch_model.bin

3c1fc4a verified over 2 years ago

History Blame Contribute Delete

1.24 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 996f1b8b47b8013dd57c74000dc8a60daa7a36382b57bcc0b681a15841e197bd
Size of remote file:: 1.24 GB
SHA256:: b52cc9f56b4902c086f5702c2d9c247dc76fe08c9ee51df0b09dada7ed66344f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

Minami-su
/

Qwen1.5-0.5B-Chat_llamafy

Detected Pickle imports (3)

Xet Pointer Details