Instructions to use LinkRur/Qwen_2.5_1.5B_Instruct_Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LinkRur/Qwen_2.5_1.5B_Instruct_Vision with Transformers:

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

pipe = pipeline("text-generation", model="LinkRur/Qwen_2.5_1.5B_Instruct_Vision", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LinkRur/Qwen_2.5_1.5B_Instruct_Vision", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("LinkRur/Qwen_2.5_1.5B_Instruct_Vision", trust_remote_code=True)
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 LinkRur/Qwen_2.5_1.5B_Instruct_Vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LinkRur/Qwen_2.5_1.5B_Instruct_Vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LinkRur/Qwen_2.5_1.5B_Instruct_Vision",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LinkRur/Qwen_2.5_1.5B_Instruct_Vision

SGLang

How to use LinkRur/Qwen_2.5_1.5B_Instruct_Vision 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 "LinkRur/Qwen_2.5_1.5B_Instruct_Vision" \
    --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": "LinkRur/Qwen_2.5_1.5B_Instruct_Vision",
		"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 "LinkRur/Qwen_2.5_1.5B_Instruct_Vision" \
        --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": "LinkRur/Qwen_2.5_1.5B_Instruct_Vision",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LinkRur/Qwen_2.5_1.5B_Instruct_Vision with Docker Model Runner:
```
docker model run hf.co/LinkRur/Qwen_2.5_1.5B_Instruct_Vision
```

LinkRur commited on 21 days ago

Commit

78e3474

verified ·

1 Parent(s): 1c321bc

detele：file structure

Browse files

Files changed (1) hide show

README.md +0 -12

README.md CHANGED Viewed

@@ -105,17 +105,5 @@ vis_desc, response = model.generate_with_image(image, "描述这张图片", toke
 print(response)
 ```
-## 文件结构
-```
-Qwen2.5-1.5B-Vision-NSD/
-├── config.json
-├── model.safetensors          # 3.18GB, 包含全部权重
-├── modeling_qwen2_vision.py
-├── tokenizer.json
-├── vocab.json / merges.txt
-└── generation_config.json
-```
 ## License
 MIT

 print(response)
 ```
 ## License
 MIT