Instructions to use deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct")
model = AutoModelForCausalLM.from_pretrained("deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct

SGLang

How to use deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct 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 "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct" \
    --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": "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct",
		"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 "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct" \
        --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": "deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct with Docker Model Runner:
```
docker model run hf.co/deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct
```

llmz commited on Nov 11, 2024

Commit

6e2a77e

verified ·

1 Parent(s): 310a9d6

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -9

README.md CHANGED Viewed

@@ -61,20 +61,16 @@ Here provides a code snippet with `apply_chat_template` to show you how to load
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "Qwen/Qwen2.5-3B-Instruct"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "Give me a short introduction to large language model."
 messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
     messages,
     tokenize=False,
@@ -91,6 +87,8 @@ generated_ids = [
 ]
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```

 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = 'deeptoken/Qwen2.5-3B-RuoZhiBa-Instruct'
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype='auto', device_map='auto')
 tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = '鸡柳是鸡的哪个部位？'
 messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
     messages,
     tokenize=False,
 ]
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
 ```