---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- quantization
- fp4
- nvfp4
- compressed-tensors
- qwen2
- text-generation
- 4bit
base_model: Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-14B-Instruct-FP4-W4A4
  results: []
quantization:
  quant_method: compressed-tensors
  bits: 4
  type: float
  format: nvfp4-pack-quantized
  strategy: tensor_group
  group_size: 16
  symmetric: true
---

# Qwen2.5-14B-Instruct-FP4-W4A4

## Model Description

This is an NVFP4 (NVIDIA FP4) quantized version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) using the compressed-tensors quantization method.

- **Base Model**: Qwen/Qwen2.5-14B-Instruct
- **Quantization Method**: compressed-tensors
- **Quantization Type**: NVFP4 W4A4 (4-bit Weight and Activation)
- **Model Size**: ~10.5GB (compared to ~28GB for BF16)
- **Compression Ratio**: ~2.7x

## Quantization Configuration

This model uses **NVFP4 (NVIDIA FP4) quantization** with grouped quantization for both weights and activations:

### Weights
- **Precision**: NVFP4 (4-bit floating point)
- **Strategy**: Tensor-group (grouped quantization)
- **Group Size**: 16
- **Symmetric**: Yes
- **Dynamic**: No (static quantization)
- **Observer**: MinMax

### Activations
- **Precision**: NVFP4 (4-bit floating point)
- **Strategy**: Tensor-group (grouped quantization)
- **Group Size**: 16
- **Symmetric**: Yes
- **Dynamic**: Local (dynamic quantization with local calibration)
- **Observer**: MinMax

### Other Details
- **Format**: nvfp4-pack-quantized (packed 4-bit format)
- **KV Cache**: Not quantized
- **Ignored Layers**: lm_head
- **Target Layers**: Linear layers
- **Quantization Version**: 0.11.0

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "JongYeop/Qwen2.5-14B-Instruct-FP4-W4A4"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

# Generate text
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**: 5120
- **Intermediate Size**: 13824
- **Number of Layers**: 48
- **Number of Attention Heads**: 40
- **Number of KV Heads**: 8
- **Vocabulary Size**: 152064
- **Max Position Embeddings**: 32768

## Intended Use

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

- Resource-constrained environments
- Edge deployment
- Applications requiring minimal memory usage
- High throughput scenarios
- GPU inference with FP4 support

## Limitations

- FP4 quantization may result in more accuracy loss compared to FP8 or INT8 quantization
- Best performance is achieved on hardware with native FP4 support (e.g., NVIDIA H100, Ada Lovelace, Blackwell GPUs)
- Dynamic activation quantization may introduce additional runtime overhead
- Grouped quantization requires compatible inference engines

## Performance Notes

- **Memory Usage**: ~2.7x reduction compared to BF16
- **Speed**: Requires hardware with FP4 tensor core support for optimal performance
- **Accuracy**: May experience some degradation compared to higher precision formats

## Citation

If you use this model, please cite the original Qwen2.5 paper and the compressed-tensors library.

## License

Same as the base model: [Apache 2.0](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)