Instructions to use Qwen/CodeQwen1.5-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/CodeQwen1.5-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/CodeQwen1.5-7B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
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 Qwen/CodeQwen1.5-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/CodeQwen1.5-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/CodeQwen1.5-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/CodeQwen1.5-7B-Chat

SGLang

How to use Qwen/CodeQwen1.5-7B-Chat 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 "Qwen/CodeQwen1.5-7B-Chat" \
    --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": "Qwen/CodeQwen1.5-7B-Chat",
		"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 "Qwen/CodeQwen1.5-7B-Chat" \
        --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": "Qwen/CodeQwen1.5-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/CodeQwen1.5-7B-Chat with Docker Model Runner:
```
docker model run hf.co/Qwen/CodeQwen1.5-7B-Chat
```

This model is Awesome

#20

by areumtecnologia - opened May 15, 2024

Discussion

areumtecnologia

May 15, 2024

You can say what you want, but among all the most used models, I haven't yet found one that is as effective. The Qwen1.5-7B-Chat works well with bitsandbytes (Q4 and Q8) and still maintains incredible attention. I tested several models in my projects, but the Qwen1.5-7B-Chat is unbeatable. The best feature of all is that it understands the user's language on the first interaction, something that other latest generation models only do through requests. Congratulations and greetings to the Qwen development team. I hope this team continues to develop this increasingly better and open-source model.
To contribute, here is a code to facilitate the use of different models, including Qwen:

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

class LLMQ4:
# Load 6.188 GB
def init(self, repo_id):
# Definindo a configuração para evitar fragmentação
# os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
# Model name
model_name = repo_id
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer, self.model = self.initialize_model(model_name)

def initialize_model(self, model_name):
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
    )

    # Tokenizer initialization
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map=self.device, torch_dtype=torch.bfloat16, quantization_config=bnb_config)
    return tokenizer, model

def prompt(self, context):
    
    # CONTEXT DEVE TER CONFIGURACAO POR ROLE
    text = self.tokenizer.apply_chat_template(
        context,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device)
    generated_ids = self.model.generate(
        model_inputs.input_ids,
        attention_mask=model_inputs['attention_mask'],
        max_new_tokens=38000,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
        repetition_penalty=1.1,
        eos_token_id=[
            self.tokenizer.eos_token_id,
        ],
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    return response

Usage:

llm = LLMQ4('Qwen/Qwen1.5-7B-Chat')
chat_history=[
# Personality SystemPrompts
{"role": "system", "content": "Your name is Jarvis."},
{"role": "user", "content": "What your name?"},
]
response = llm.prompt(chat_history)

chat_history.append({"role": "assistant", "content": response})
chat_history.append({"role": "user", "content": "Do a serach on the internet about..."})
response = llm.prompt(chat_history)

...