README.md

---
license: other
license_name: databricks-open-model-license
license_link: https://www.databricks.com/legal/open-model-license
base_model: databricks/dbrx-instruct
---
# dbrx-instruct-FP8-KV
- ## Introduction
  This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) with calibration samples from Pile dataset.
- ## Quantization Stragegy
  - ***Quantized Layers***: All linear layers excluding "lm_head" and "router.layer"
  - ***Weight***: FP8 symmetric per-tensor
  - ***Activation***: FP8 symmetric per-tensor
  - ***KV Cache***: FP8 symmetric  per-tensor
- ## Quick Start
1. [Download and install Quark](https://quark.docs.amd.com/latest/install.html)
2. Run the quantization script in the example folder using the following command line:
```sh
export MODEL_DIR = [local model checkpoint folder] or databricks/dbrx-instruct
# single GPU
python3 quantize_quark.py \ 
        --model_dir $MODEL_DIR \
        --output_dir dbrx-instruct-FP8-KV \                           
        --quant_scheme w_fp8_a_fp8 \
        --kv_cache_dtype fp8 \
        --num_calib_data 128 \
        --model_export quark_safetensors \
        --no_weight_matrix_merge \
        --custom_mode fp8
# If model size is too large for single GPU, please use multi GPU instead.
python3 quantize_quark.py
        --model_dir $MODEL_DIR \
        --output_dir dbrx-instruct-FP8-KV\
        --quant_scheme w_fp8_a_fp8 \
        --kv_cache_dtype fp8 \
        --num_calib_data 128 \
        --multi_gpu \
        --model_export quark_safetensors \
        --no_weight_matrix_merge \
        --custom_mode fp8
```
## Deployment
Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the vLLM backend(vLLM-compatible).
In the dbrx-instruct model, "transformer.blocks.\*.ffn.experts" modules can be divided into experts-num mlps, and if the shape of the weight of w1 in one of the mlps is [dim1, dim2], 
then the shape of “transformer.blocks.\*.ffn.experts.mlp.w1.weight“ in the exported safetensors file is [dim1\*experts-num, dim2]. The shapes of "transformer.blocks.\*.ffn.experts.mlp.w1.weight_scale" 
and "transformer.blocks.\*.ffn.experts.mlp.w1.input_scale" are [dim1]. Similarly, this also applies to the w2 and v1 of "transformer.blocks.\*.ffn.experts.mlp".
## Evaluation
Quark currently uses perplexity(PPL) as the evaluation metric for accuracy loss before and after quantization.The specific PPL algorithm can be referenced in the quantize_quark.py.
The quantization evaluation results are conducted in pseudo-quantization mode, which may slightly differ from the actual quantized inference accuracy. These results are provided for reference only.
#### Evaluation scores
<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>dbrx-instruct </strong>
   </td>
   <td><strong>dbrx-instruct-FP8-KV(this model)</strong>
   </td>
  </tr>
  <tr>
   <td>Perplexity-wikitext2
   </td>
   <td>4.2275
   </td>
   <td>4.3033
   </td>
  </tr>
  
</table>

#### License
Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.