| # Export and Push | |
| ## Merge LoRA | |
| - See [here](https://github.com/modelscope/ms-swift/blob/main/examples/export/merge_lora.sh). | |
| ## Quantization | |
| SWIFT supports quantization exports for AWQ, GPTQ, FP8, and BNB models. AWQ and GPTQ require a calibration dataset, which yields better quantization performance but takes longer to quantize. On the other hand, FP8 and BNB does not require a calibration dataset and is quicker to quantize. | |
| | Quantization Technique | Multimodal | Inference Acceleration | Continued Training | | |
| | ---------------------- | ---------- | ---------------------- | ------------------ | | |
| | GPTQ | ✅ | ✅ | ✅ | | |
| | AWQ | ✅ | ✅ | ✅ | | |
| | BNB | ❌ | ✅ | ✅ | | |
| In addition to the SWIFT installation, the following additional dependencies need to be installed: | |
| ```shell | |
| # For AWQ quantization: | |
| # The versions of autoawq and CUDA are correlated; please choose the version according to `https://github.com/casper-hansen/AutoAWQ`. | |
| # If there are dependency conflicts with torch, please add the `--no-deps` option. | |
| pip install autoawq -U | |
| # For GPTQ quantization: | |
| # The versions of auto_gptq and CUDA are correlated; please choose the version according to `https://github.com/PanQiWei/AutoGPTQ#quick-installation`. | |
| pip install auto_gptq optimum -U | |
| # For BNB quantization: | |
| pip install bitsandbytes -U | |
| ``` | |
| We provide a series of scripts to demonstrate SWIFT's quantization export capabilities: | |
| - Supports [AWQ](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/awq.sh)/[GPTQ](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/gptq.sh)/[BNB](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/bnb.sh) quantization exports. | |
| - Multimodal quantization: Supports quantizing multimodal models using GPTQ and AWQ, with limited multimodal models supported by AWQ. Refer to [here](https://github.com/modelscope/ms-swift/tree/main/examples/export/quantize/mllm). | |
| - Support for more model series: Supports quantization exports for [BERT](https://github.com/modelscope/ms-swift/tree/main/examples/export/quantize/bert) and [Reward Model](https://github.com/modelscope/ms-swift/tree/main/examples/export/quantize/reward_model). | |
| - Models exported with SWIFT's quantization support inference acceleration using vllm/sglang/lmdeploy; they also support further SFT/RLHF using QLoRA. | |
| ## Push Model | |
| SWIFT supports re-pushing trained/quantized models to ModelScope/Hugging Face. By default, it pushes to ModelScope, but you can specify `--use_hf true` to push to Hugging Face. | |
| ```shell | |
| swift export \ | |
| --model output/vx-xxx/checkpoint-xxx \ | |
| --push_to_hub true \ | |
| --hub_model_id '<model-id>' \ | |
| --hub_token '<sdk-token>' \ | |
| --use_hf false | |
| ``` | |
| Tips: | |
| - You can use `--model <checkpoint-dir>` or `--adapters <checkpoint-dir>` to specify the checkpoint directory to be pushed. There is no difference between these two methods in the model pushing scenario. | |
| - When pushing to ModelScope, you need to make sure you have registered for a ModelScope account. Your SDK token can be obtained from [this page](https://www.modelscope.cn/my/myaccesstoken). Ensure that the account associated with the SDK token has edit permissions for the organization corresponding to the model_id. The model pushing process will automatically create a model repository corresponding to the model_id (if it does not already exist), and you can use `--hub_private_repo true` to automatically create a private model repository. | |