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---
license: apache-2.0
base_model: lmzheng/grok-1
---
# Grok-1-W4A8KV8
## 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", "*.gate"
- ***Weight***: FP8 symmetric per-tensor, additionally, INT4 symmetric per-channel for MoE linear
- ***Activation***: FP8 symmetric per-tensor
- ***KV Cache***: FP8 symmetric per-tensor
### INT4 Packing
Every eight `int4` values are packed into a single `int32` integeter following the sequence defined by `order_map = [0, 2, 4, 6, 1, 3, 5, 7]`.
## 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 lmzheng/grok-1
python3 quantize_quark.py \
--model_dir $MODEL_DIR \
--output_dir grok-1-W4A8KV8 \
--quant_scheme TBD \
--kv_cache_dtype fp8 \
--num_calib_data 128 \
--model_export hf_format \
--multi_gpu \
--custom_mode fp8
```
## Deployment
Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the SGLang backend.
## 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>grok-1 </strong>
</td>
<td><strong>grok-1-W4A8KV8(this model)</strong>
</td>
</tr>
<tr>
<td>Perplexity-wikitext2
</td>
<td>TBD
</td>
<td>TBD
</td>
</tr>
</table>
#### License
Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.
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