Instructions to use JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4 with Transformers:

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

pipe = pipeline("text-generation", model="JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4")
model = AutoModelForCausalLM.from_pretrained("JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4")
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 JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4

SGLang

How to use JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4 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 "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4" \
    --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": "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4",
		"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 "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4" \
        --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": "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4 with Docker Model Runner:
```
docker model run hf.co/JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4
```

Qwen2.5-7B-Instruct-MXFP4-W4A4 / README.md

JongYeop

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	---
	language:
	- en
	- zh
	license: apache-2.0
	library_name: transformers
	tags:
	- quantization
	- fp4
	- mxfp4
	- compressed-tensors
	- qwen2
	- text-generation
	- 4bit
	base_model: Qwen/Qwen2.5-7B-Instruct
	pipeline_tag: text-generation
	model-index:
	- name: Qwen2.5-7B-Instruct-MXFP4-W4A4
	results: []
	quantization:
	quant_method: compressed-tensors
	bits: 4
	type: float
	format: mxfp4-pack-quantized
	strategy: group
	group_size: 32
	symmetric: true
	---

	# Qwen2.5-7B-Instruct-MXFP4-W4A4

	## Model Description

	This is an MXFP4 (Microscaling FP4) quantized version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using the compressed-tensors quantization method.

	- Base Model: Qwen/Qwen2.5-7B-Instruct
	- Quantization Method: compressed-tensors
	- Quantization Type: MXFP4 W4A4 (4-bit Weight and Activation)
	- Format: mxfp4-pack-quantized (MX Microscaling FP4)
	- Model Size: ~5.3GB (compared to ~15GB for BF16)
	- Compression Ratio: ~2.8x

	## Quantization Configuration

	This model uses MXFP4 (Microscaling FP4) quantization with block-scaled quantization (group size 32) for both weights and activations. MXFP4 uses E8M0 (8-bit exponent-only) block scales shared across groups of 32 elements, following the [OCP MX specification](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf).

	### Weights
	- Precision: FP4 E2M1 (4-bit floating point)
	- Scale Format: E8M0 (uint8 exponent)
	- Strategy: Group (block-scaled)
	- Group Size: 32
	- Symmetric: Yes
	- Dynamic: No (static quantization with calibration)

	### Activations
	- Precision: FP4 E2M1 (4-bit floating point)
	- Scale Format: E8M0 (uint8 exponent)
	- Strategy: Group (block-scaled)
	- Group Size: 32
	- Symmetric: Yes
	- Dynamic: Yes (dynamic quantization at inference time)

	### Other Details
	- KV Cache: Not quantized (remains in BF16)
	- Ignored Layers: lm_head
	- Target Layers: All Linear layers
	- Calibration: 512 samples from CNN/DailyMail, max_seq_length=2048

	## Hardware Requirements

	MXFP4 inference requires NVIDIA Blackwell (SM120+) GPUs with CUDA 12.8+ for native CUTLASS MXFP4 GEMM support.

	## Usage with vLLM

	```python
	from vllm import LLM, SamplingParams

	model_id = "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4"

	llm = LLM(model=model_id, max_model_len=4096, enforce_eager=True)

	outputs = llm.generate(
	["The capital of France is"],
	SamplingParams(max_tokens=64, temperature=0)
	)

	for output in outputs:
	print(output.outputs[0].text)
	```

	## Usage with Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "JongYeop/Qwen2.5-7B-Instruct-MXFP4-W4A4"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	torch_dtype="auto"
	)

	messages = [
	{"role": "user", "content": "What is machine learning?"}
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	outputs = model.generate(
	input_ids,
	max_new_tokens=256,
	do_sample=True,
	temperature=0.7,
	top_p=0.9,
	)

	response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
	print(response)
	```

	## Model Architecture

	- Architecture: Qwen2ForCausalLM
	- Hidden Size: 3584
	- Intermediate Size: 18944
	- Number of Layers: 28
	- Number of Attention Heads: 28
	- Number of KV Heads: 4 (GQA)
	- Vocabulary Size: 152064
	- Max Position Embeddings: 32768

	## Differences from NVFP4

	\| Feature \| MXFP4 \| NVFP4 \|
	\|---------\|-------\|-------\|
	\| Scale Format \| E8M0 (uint8 exponent) \| E4M3 + FP32 global scale \|
	\| Group Size \| 32 \| 16 \|
	\| Standard \| OCP MX Specification \| NVIDIA proprietary \|
	\| Hardware \| SM120+ (Blackwell) \| SM89+ (Ada/Hopper/Blackwell) \|

	## Intended Use

	This quantized model is intended for efficient inference with significantly reduced memory footprint. It is suitable for:

	- Deployment on NVIDIA Blackwell GPUs
	- Memory-constrained serving environments
	- High-throughput inference scenarios

	## Limitations

	- Requires NVIDIA Blackwell (SM120+) GPUs for native MXFP4 GEMM support
	- FP4 quantization may result in some accuracy degradation compared to FP8 or BF16
	- KV cache remains in BF16 (not quantized)

	## License

	Same as the base model: [Apache 2.0](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)