Instructions to use drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="drawais/Qwen2.5-Coder-32B-Instruct-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drawais/Qwen2.5-Coder-32B-Instruct-NVFP4")
model = AutoModelForCausalLM.from_pretrained("drawais/Qwen2.5-Coder-32B-Instruct-NVFP4")
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 drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drawais/Qwen2.5-Coder-32B-Instruct-NVFP4

SGLang

How to use drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 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 "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4" \
    --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": "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4",
		"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 "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4" \
        --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": "drawais/Qwen2.5-Coder-32B-Instruct-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 with Docker Model Runner:
```
docker model run hf.co/drawais/Qwen2.5-Coder-32B-Instruct-NVFP4
```

Qwen2.5-Coder-32B-Instruct-NVFP4 / README.md

drawais

Initial upload of Qwen2.5-Coder-32B-Instruct-NVFP4

9db2d96 verified 1 day ago

preview code

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metadata

license: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0
base_model: Qwen/Qwen2.5-Coder-32B-Instruct
tags:
  - quantized
  - 4-bit
  - int4
  - awq
language:
  - en
library_name: transformers
pipeline_tag: text-generation

Qwen2.5-Coder-32B-Instruct-NVFP4

INT4 weight-only quantization of Qwen/Qwen2.5-Coder-32B-Instruct.

Qwen2.5-Coder-32B-Instruct in NVFP4 W4A4. Native vLLM compressed-tensors. About 17 GB on disk.

Property	Value
Base model	Qwen/Qwen2.5-Coder-32B-Instruct
Quantization	INT4 weight-only
Approx. on-disk size	~20.7 GB
License	Apache License, Version 2.0
Languages	English

Load (vLLM)

vllm serve drawais/Qwen2.5-Coder-32B-Instruct-NVFP4 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.94

from vllm import LLM, SamplingParams
llm = LLM(model="drawais/Qwen2.5-Coder-32B-Instruct-NVFP4", max_model_len=32768)
print(llm.generate(["Hello!"], SamplingParams(max_tokens=128))[0].outputs[0].text)

Footprint

~20.7 GB on disk. Recommended VRAM: enough headroom for KV cache.

License & attribution

This artifact is a derivative work of Qwen/Qwen2.5-Coder-32B-Instruct, released by its original authors under the Apache License, Version 2.0.

This artifact is distributed under the same license. The full license text is included in LICENSE, and required attribution is in NOTICE.

License text: https://www.apache.org/licenses/LICENSE-2.0 Source model: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct