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 Settings

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
```

TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture

#43

by vigneshwar234 - opened about 20 hours ago

Discussion

vigneshwar234

about 20 hours ago

New transformer architecture: TMT — dynamic graph attention + per-token adaptive depth

TemporalMesh Transformer (TMT) achieves 29.4 PPL on WikiText-2 at 48% compute (120M params) — outperforming Mamba (31.8), RWKV (33.1), Longformer (39.6), and vanilla transformer (42.1).

5 innovations unified in one forward pass:

Innovation	What it does	Cost
Mesh Attention	Dynamic kNN graph per layer from cosine similarity	O(S·k) vs O(S²)
Temporal Decay	Learned multiplicative attenuation post-softmax	~0 overhead
Adaptive Exit	Per-token gate: punctuation exits layer 2, rare words layer 12	−52% compute
Dual-Stream FFN	Syntax + semantic parallel MLP streams	Same FLOPs
EMA Anchors	16 persistent fast-weight vectors, β=0.99	32KB params

Cross-benchmark results:

WikiText-103: 36.1 PPL vs 38.4 Mamba
LongBench: 53.4 vs 51.3 Mamba
C4: 27.4 PPL vs 30.1 Mamba
The Pile: 35.8 PPL
226 tests passing, 3 seeds (42/1337/2024), full ablations

Superadditive synergy: Combined gain = 12.7 PPL vs 8.6 from summing individual components.

📄 Paper: https://zenodo.org/records/20287390 (DOI: 10.5281/zenodo.20287197)
💻 Code: https://github.com/vignesh2027/TemporalMesh-Transformer
🎮 Live demo: https://huggingface.co/spaces/vigneshwar234/TemporalMesh-Transformer-Demo
🤗 Model + benchmarks: https://huggingface.co/vigneshwar234/TemporalMesh-Transformer

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