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DeepSeek-V3.1-int4-AutoRound

Text Generation
Safetensors
deepseek_v3
conversational
custom_code
4-bit precision
auto-round
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DeepSeek-V3.1-int4-AutoRound
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---
base_model:
- deepseek-ai/DeepSeek-V3.1
pipeline_tag: text-generation
---

## Model Details

This model is a int4 model with group_size 128 and symmetric quantization of [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) generated by [intel/auto-round](https://github.com/intel/auto-round).
Please follow the license of the original model.

## How To Use


### Generate the model

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

model_name = "deepseek-ai/DeepSeek-V3.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=False, torch_dtype="auto")

block = model.model.layers
device_map = {}

for n, m in block.named_modules():
    if isinstance(m, (torch.nn.Linear, transformers.modeling_utils.Conv1D)):
        if "experts" in n and ("shared_experts" not in n) and int(n.split('.')[-2]) < 63:
            device = "cuda:1"
        elif "experts" in n and ("shared_experts" not in n) and int(n.split('.')[-2]) >= 63 and int(
                n.split('.')[-2]) < 128:
            device = "cuda:2"
        elif "experts" in n and ("shared_experts" not in n) and int(n.split('.')[-2]) >= 128 and int(
                n.split('.')[-2]) < 192:
            device = "cuda:3"
        elif "experts" in n and ("shared_experts" not in n) and int(
                n.split('.')[-2]) >= 192:
            device = "cuda:4"
        else:
            device = "cuda:0"
        n = n[2:]

        device_map.update({n: device})


from auto_round import AutoRound

autoround = AutoRound(model=model, tokenizer=tokenizer, device_map=device_map, nsamples=512,
                      batch_size=4, low_gpu_mem_usage=True, seqlen=2048,
                      )
autoround.quantize_and_save(format="auto_round", output_dir="tmp_autoround")
```


## 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 [link](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)