---
base_model:
- Qwen/Qwen3-Next-80B-A3B-Instruct
pipeline_tag: text-generation
license: apache-2.0
---

## Model Details

This model is a mixed int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round) **via RTN(no algorithm tuning)**. 
Non expert layers are fallback to 8 bits. Please refer to Section Generate the model for more details.
Please follow the license of the original model.

## How To Use

For vllm, this pr is required https://github.com/vllm-project/vllm/pull/24818

### INT4 Inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
  model_name,
  dtype="auto",
  device_map="auto",
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)
"""
content: A large language model (LLM) is a type of artificial intelligence system trained on vast amounts of text data to understand and generate human-like language. These models, such as GPT, PaLM, or LLaMA, use deep learning architectures—typically based on the transformer network—to predict the next word in a sequence, enabling them to answer questions, write essays, translate languages, and even code. LLMs learn patterns, context, and relationships in language without explicit programming, making them versatile tools for a wide range of natural language tasks. Their scale—often with billions or trillions of parameters—allows them to capture nuanced linguistic features, though they also require significant computational resources and raise important ethical and safety considerations.
"""
```

### Generate the model

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
from auto_round import AutoRound

model_name = "Qwen/Qwen3-Next-80B-A3B-Instruct"

layer_config = {}
for n, m in model.named_modules():
    if isinstance(m, torch.nn.Linear):
        if "expert" in n and "shared_experts" not in n:
            layer_config[n] = {"bits": 4}
            print(n, 4)
        elif n != "lm_head":
            layer_config[n] = {"bits": 8}
            print(n, 8)

autoround = AutoRound(model_name, iters=0, layer_config=layer_config)
autoround.quantize_and_save(format="auto_round", output_dir="tmp_autoround")

```

## Evaluate Results

| benchmark | n-shot | backend | Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound | Qwen/Qwen3-Next-80B-A3B-Instruct |
| :-------: | :----: | :-----: | :----------------------------------------------------: | :------------------------------: |
|   gsm8k   |   5    |  vllm   |                         0.8393                         |              0.8074              |
| mmlu_pro  |   5    |  vllm   |                         0.7630                         |              0.7621              |

## Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

## Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

- [Intel Neural Compressor](https://github.com/intel/neural-compressor)

## Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

## Cite

@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }

[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)