Instructions to use Qwen/Qwen2.5-Coder-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2.5-Coder-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qwen/Qwen2.5-Coder-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2.5-Coder-32B-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": "Qwen/Qwen2.5-Coder-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen2.5-Coder-32B-Instruct

SGLang

How to use Qwen/Qwen2.5-Coder-32B-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 "Qwen/Qwen2.5-Coder-32B-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": "Qwen/Qwen2.5-Coder-32B-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 "Qwen/Qwen2.5-Coder-32B-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": "Qwen/Qwen2.5-Coder-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen2.5-Coder-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2.5-Coder-32B-Instruct
```

Update README.md

by AngelVenerov - opened Nov 13, 2024

base: refs/heads/main

←

from: refs/pr/9

Discussion Files changed

-4

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -5,7 +5,7 @@ language:
 - en
 base_model:
 - Qwen/Qwen2.5-Coder-32B
-pipeline_tag: text-generation
 library_name: transformers
 tags:
 - code
@@ -32,7 +32,7 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
 - Number of Parameters: 32.5B
 - Number of Paramaters (Non-Embedding): 31.0B
-- Number of Layers: 64
 - Number of Attention Heads (GQA): 40 for Q and 8 for KV
 - Context Length: Full 131,072 tokens
   - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
@@ -78,7 +78,7 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 generated_ids = model.generate(
     **model_inputs,
-    max_new_tokens=512
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
@@ -132,4 +132,4 @@ If you find our work helpful, feel free to give us a cite.
       journal={arXiv preprint arXiv:2407.10671},
       year={2024}
 }
-```

 - en
 base_model:
 - Qwen/Qwen2.5-Coder-32B
+pipeline_tag: image-to-text
 library_name: transformers
 tags:
 - code
 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
 - Number of Parameters: 32.5B
 - Number of Paramaters (Non-Embedding): 31.0B
+- Number of Layers: 512
 - Number of Attention Heads (GQA): 40 for Q and 8 for KV
 - Context Length: Full 131,072 tokens
   - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
 generated_ids = model.generate(
     **model_inputs,
+    max_new_tokens=4096
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
       journal={arXiv preprint arXiv:2407.10671},
       year={2024}
 }
+```