modelscope/ms-swift
Oct 2024 – Oct 2025
13 Months active
Languages Used
MarkdownPythonShellJupyter NotebookRSTYAMLTorchJinja2
Technical Skills
Deep LearningDocumentation UpdateMachine LearningModel ConfigurationModel RegistrationModel Training
Jintao
PROFILE
Jintao
Huang Jintao engineered large-scale model training, inference, and deployment workflows for the modelscope/ms-swift repository, focusing on expanding multimodal and LLM support while improving reliability and developer experience. He implemented features such as Megatron-based distributed training, adapter-based fine-tuning, and quantization workflows, integrating technologies like PyTorch and DeepSpeed to optimize performance and scalability. His work included robust data handling, template engineering, and compatibility layers for evolving model architectures, enabling seamless onboarding of new models and efficient experimentation. Through extensive code refactoring, documentation updates, and targeted bug fixes, Huang delivered production-ready pipelines that accelerated model iteration and supported diverse deployment scenarios.
Overall Statistics
Feature vs Bugs
54%Features
Repository Contributions
848Total
Bugs
298
Commits
848
Features
350
Lines of code
107,066
Activity Months13
Your Network
301 peopleSame Organization
@alibaba-inc.com271
Work History
October 2025
51 Commits • 26 Features
Oct 1, 2025October 2025 (2025-10) – ms-swift (modelscope/ms-swift) delivered expanded model support, reliability improvements, and deployment readiness. Key features delivered include GLM4.6, DeepSeek-V3.1-Terminus, and Qwen/Qwen3-VL-30B-Instruct/Thinking model support, enabling rapid onboarding of new models for production inference. A targeted set of bug fixes and stability improvements were completed to boost reliability and user experience.
51 Commits • 26 Features
Oct 1, 2025October 2025 (2025-10) – ms-swift (modelscope/ms-swift) delivered expanded model support, reliability improvements, and deployment readiness. Key features delivered include GLM4.6, DeepSeek-V3.1-Terminus, and Qwen/Qwen3-VL-30B-Instruct/Thinking model support, enabling rapid onboarding of new models for production inference. A targeted set of bug fixes and stability improvements were completed to boost reliability and user experience.
October 2025
September 2025
71 Commits • 36 Features
Sep 1, 2025September 2025 performance summary for repository modelscope/ms-swift. Delivered broad Megatron-based multimodal capabilities, template enhancements, and stability improvements across the model/template ecosystem, enabling faster go-to-market for multimodal solutions.
September 2025
71 Commits • 36 Features
Sep 1, 2025September 2025 performance summary for repository modelscope/ms-swift. Delivered broad Megatron-based multimodal capabilities, template enhancements, and stability improvements across the model/template ecosystem, enabling faster go-to-market for multimodal solutions.
August 2025
85 Commits • 52 Features
Aug 1, 2025August 2025 (2025-08) monthly summary for modelscope/ms-swift. This month focused on expanding model compatibility, boosting training efficiency, and enhancing robustness across inference and training pipelines. Key features were delivered to broaden model support, improve attention performance, and empower more flexible model fine-tuning workflows. The efforts translated into faster time-to-value for model deployment and more reliable large-scale training runs. Key deliverables: - Expanded multi-model backend: Added support for Qwen/Qwen3-Coder-30B-A3B-Instruct, Hunyuan-7B-Instruct series, and OVIS2.5, alongside broader model interoperability with GPT OSS-20B, minicpmv4, Qwen-3-4B-Instruct-2507, and GLM-4.5V. These updates reduce integration risk and enable teams to test a wider set of models with the same training/inference stack. - Megatron performance enhancements: Implemented FlashAttention-3 support in Megatron and the training chain, delivering faster attention computation and improved memory efficiency for large-scale models. - Training and inference workflow improvements: Added DPO adapters, KTO/GRPO adapters, training adapters, and ref_adapters in RLHF workflows; introduced DeepSpeed launcher support and Qwen3 Thinking integration to streamline distributed training and inference scenarios. - Core optimization and reliability: MCore load path optimizations and test-precision optimizations improved startup and runtime efficiency; rope_scaling refactor enhances training throughput. Infrastructure updates include Swift image upgrades and refreshed requirements for security and compatibility, plus targeted bug fixes (e.g., vllm compatibility, reward_model integration, and interval/new tokens handling) to stabilize end-to-end pipelines. - Documentation, templates, and shell improvements: Template improvements (loss_scale handling, extra_kwargs simplifications), documentation updates, and shell enhancements with cached dataset examples and updated models for smoother developer experience. Overall impact: This period delivered broader model support, faster and more stable training/inference pipelines, and stronger governance over adapter-based fine-tuning, enabling faster experimentation, safer rollouts, and improved enterprise readiness for large-scale LLM deployments. Technologies/skills demonstrated: DeepSpeed/Megatron integration, FlashAttention-3, model backend integration, adapters (DPO, RLHF), training pipelines, data templates, and infrastructure automation (image/requirements updates).
85 Commits • 52 Features
Aug 1, 2025August 2025 (2025-08) monthly summary for modelscope/ms-swift. This month focused on expanding model compatibility, boosting training efficiency, and enhancing robustness across inference and training pipelines. Key features were delivered to broaden model support, improve attention performance, and empower more flexible model fine-tuning workflows. The efforts translated into faster time-to-value for model deployment and more reliable large-scale training runs. Key deliverables: - Expanded multi-model backend: Added support for Qwen/Qwen3-Coder-30B-A3B-Instruct, Hunyuan-7B-Instruct series, and OVIS2.5, alongside broader model interoperability with GPT OSS-20B, minicpmv4, Qwen-3-4B-Instruct-2507, and GLM-4.5V. These updates reduce integration risk and enable teams to test a wider set of models with the same training/inference stack. - Megatron performance enhancements: Implemented FlashAttention-3 support in Megatron and the training chain, delivering faster attention computation and improved memory efficiency for large-scale models. - Training and inference workflow improvements: Added DPO adapters, KTO/GRPO adapters, training adapters, and ref_adapters in RLHF workflows; introduced DeepSpeed launcher support and Qwen3 Thinking integration to streamline distributed training and inference scenarios. - Core optimization and reliability: MCore load path optimizations and test-precision optimizations improved startup and runtime efficiency; rope_scaling refactor enhances training throughput. Infrastructure updates include Swift image upgrades and refreshed requirements for security and compatibility, plus targeted bug fixes (e.g., vllm compatibility, reward_model integration, and interval/new tokens handling) to stabilize end-to-end pipelines. - Documentation, templates, and shell improvements: Template improvements (loss_scale handling, extra_kwargs simplifications), documentation updates, and shell enhancements with cached dataset examples and updated models for smoother developer experience. Overall impact: This period delivered broader model support, faster and more stable training/inference pipelines, and stronger governance over adapter-based fine-tuning, enabling faster experimentation, safer rollouts, and improved enterprise readiness for large-scale LLM deployments. Technologies/skills demonstrated: DeepSpeed/Megatron integration, FlashAttention-3, model backend integration, adapters (DPO, RLHF), training pipelines, data templates, and infrastructure automation (image/requirements updates).
August 2025
July 2025
92 Commits • 44 Features
Jul 1, 2025July 2025 performance summary for repo modelscope/ms-swift. Focused on delivering stability, scalability, and broader model support across training, inference, and documentation. The month included critical reliability fixes, targeted feature work, and significant refactors to packing and resume workflows, enabling more predictable long-running runs and easier maintenance. Key business outcomes include improved data utilization, faster experimentation cycles, and expanded model/token support for production-grade workloads.
July 2025
92 Commits • 44 Features
Jul 1, 2025July 2025 performance summary for repo modelscope/ms-swift. Focused on delivering stability, scalability, and broader model support across training, inference, and documentation. The month included critical reliability fixes, targeted feature work, and significant refactors to packing and resume workflows, enabling more predictable long-running runs and easier maintenance. Key business outcomes include improved data utilization, faster experimentation cycles, and expanded model/token support for production-grade workloads.
June 2025
68 Commits • 28 Features
Jun 1, 2025June 2025 monthly summary for modelscope/ms-swift. The team focused on expanding training flexibility, broadening model support, and improving stability across Megatron-driven workflows, with notable enhancements in DPO, FP8 quantization, and multi-model scaling. The period also included several reliability fixes to keep production pipelines robust and aligned with evolving compatibility requirements. Key features delivered include: - Megatron: added support for num_train_epochs in Megatron training, enabling longer and more configurable training schedules. Commit 181e11ec2a8093ea8bda4bdcf403b8e56252fe41. - DPO: padding_free/logits_to_keep support and compatibility with TRL 0.18, improving training ergonomics and cross-version compatibility. Commit e060ad82fc025a436365c629cca487fd9b8fbedd. - Minicpm4 support: added broader hardware/model coverage to accelerate experimentation. Commit 392ceb1d225f51a2876f2924726cfc66c8f685db. - Megatron: rope-scaling and multi-model support, expanding the range of deployable configurations including deepseek-r1-qwen3-8b, internlm3, mimo-7b. Commit 8769f88bddca3f02eaeb16009b1e607b2cecdef5. - Megatron FP8 support and shell updates to enable quantized training paths and streamlined deployment. Commits c8bc4615e9176d87e3fcce8bb178ed64a7be3318 and 5712d6af50c6a956ede55404382cedca8251ee7c. Major bugs fixed include: - Seq_parallel: compute_acc fix for accurate performance metrics across distributed runs. Commits 3478bdbd858f65404c9acddd181c04e2a69ce45d and b9e804a49d1136705d5c3f40d899aeef779308ef. - Qwen2.5-VL use_cache fix to correct training-time caching behavior. Commit 730ecc90e01284b6e16f07b733ae47fab2f3a111. - Checkpoint symlink & GRPO Omni fix, ensuring reliable checkpointing and Omni compatibility. Commit 9dfa63a060ba6de6f53a0a00cf99c3025ea3fe18. - Megatron val_dataset fix to ensure proper val data handling. Commit 691c3d408e6240e1ccfe963581b717da8e6504ac. - VLM channel loss and VLM use_logits_to_keep fixes to correct training dynamics. Commits 9c9e9602c384333e86be45c95f66c2f6202a6eba and 560d5332df05d642220e0ffcab725269c17fcedf. - Megatron: DPO integration hooks and related packing_cache/DPOTrainer updates to stabilize DPO workflows. Commits a5dfdc2aefbac459ddeed93366a2a3351354a128 and 19b34bc5c9ee45e5ced10e3371f67037b582f944. - Other stability and dataset fixes across the Megatron ecosystem including DPO emoji dataset, grounding_dataset, and PP-level refinements. Representative commits: 3feb0bc70284c56a8d1e4d17a67ad98f6d7485b4, 66?? (omitted for brevity). Overall impact and accomplishments: The month delivered meaningful business value by enabling longer and more flexible Megatron training cycles, expanding model compatibility and deployment options, and hardening training/inference pipelines against edge cases. These efforts reduce time-to-market for model iterations, improve reliability in production training, and bolster cross-version compatibility with TRL 0.18 and Megatron Core interop. The team also advanced quantization and efficiency pathways (FP8) to reduce compute costs per training run while maintaining model quality. Technologies/skills demonstrated: - Large-scale model training orchestration (Megatron, DPO) with extended hyperparameters and compatibility layers - Distributed training robustness (seq_parallel, device_map, ddp rank handling) - Quantization and efficiency (FP8, training-time optimizations) - Ecosystem integration across Megatron, Qwen, DPO, GKD, and many model families (InternLM, DeepSeek, dots1, Tencent-Hunyuan, ERNIE, etc.) - Documentation, tooling, and template optimizations to improve developer experience and rollout capabilities
68 Commits • 28 Features
Jun 1, 2025June 2025 monthly summary for modelscope/ms-swift. The team focused on expanding training flexibility, broadening model support, and improving stability across Megatron-driven workflows, with notable enhancements in DPO, FP8 quantization, and multi-model scaling. The period also included several reliability fixes to keep production pipelines robust and aligned with evolving compatibility requirements. Key features delivered include: - Megatron: added support for num_train_epochs in Megatron training, enabling longer and more configurable training schedules. Commit 181e11ec2a8093ea8bda4bdcf403b8e56252fe41. - DPO: padding_free/logits_to_keep support and compatibility with TRL 0.18, improving training ergonomics and cross-version compatibility. Commit e060ad82fc025a436365c629cca487fd9b8fbedd. - Minicpm4 support: added broader hardware/model coverage to accelerate experimentation. Commit 392ceb1d225f51a2876f2924726cfc66c8f685db. - Megatron: rope-scaling and multi-model support, expanding the range of deployable configurations including deepseek-r1-qwen3-8b, internlm3, mimo-7b. Commit 8769f88bddca3f02eaeb16009b1e607b2cecdef5. - Megatron FP8 support and shell updates to enable quantized training paths and streamlined deployment. Commits c8bc4615e9176d87e3fcce8bb178ed64a7be3318 and 5712d6af50c6a956ede55404382cedca8251ee7c. Major bugs fixed include: - Seq_parallel: compute_acc fix for accurate performance metrics across distributed runs. Commits 3478bdbd858f65404c9acddd181c04e2a69ce45d and b9e804a49d1136705d5c3f40d899aeef779308ef. - Qwen2.5-VL use_cache fix to correct training-time caching behavior. Commit 730ecc90e01284b6e16f07b733ae47fab2f3a111. - Checkpoint symlink & GRPO Omni fix, ensuring reliable checkpointing and Omni compatibility. Commit 9dfa63a060ba6de6f53a0a00cf99c3025ea3fe18. - Megatron val_dataset fix to ensure proper val data handling. Commit 691c3d408e6240e1ccfe963581b717da8e6504ac. - VLM channel loss and VLM use_logits_to_keep fixes to correct training dynamics. Commits 9c9e9602c384333e86be45c95f66c2f6202a6eba and 560d5332df05d642220e0ffcab725269c17fcedf. - Megatron: DPO integration hooks and related packing_cache/DPOTrainer updates to stabilize DPO workflows. Commits a5dfdc2aefbac459ddeed93366a2a3351354a128 and 19b34bc5c9ee45e5ced10e3371f67037b582f944. - Other stability and dataset fixes across the Megatron ecosystem including DPO emoji dataset, grounding_dataset, and PP-level refinements. Representative commits: 3feb0bc70284c56a8d1e4d17a67ad98f6d7485b4, 66?? (omitted for brevity). Overall impact and accomplishments: The month delivered meaningful business value by enabling longer and more flexible Megatron training cycles, expanding model compatibility and deployment options, and hardening training/inference pipelines against edge cases. These efforts reduce time-to-market for model iterations, improve reliability in production training, and bolster cross-version compatibility with TRL 0.18 and Megatron Core interop. The team also advanced quantization and efficiency pathways (FP8) to reduce compute costs per training run while maintaining model quality. Technologies/skills demonstrated: - Large-scale model training orchestration (Megatron, DPO) with extended hyperparameters and compatibility layers - Distributed training robustness (seq_parallel, device_map, ddp rank handling) - Quantization and efficiency (FP8, training-time optimizations) - Ecosystem integration across Megatron, Qwen, DPO, GKD, and many model families (InternLM, DeepSeek, dots1, Tencent-Hunyuan, ERNIE, etc.) - Documentation, tooling, and template optimizations to improve developer experience and rollout capabilities
June 2025
May 2025
72 Commits • 34 Features
May 1, 20252025-05 monthly summary for modelscope/ms-swift: Delivered broad model integration, stability improvements, and documentation enhancements that collectively expand model compatibility, accelerate experimentation, and improve training reliability. Highlights include multi-model support, improved training configurability, and targeted fixes across distributed training, packing, and data handling.
May 2025
72 Commits • 34 Features
May 1, 20252025-05 monthly summary for modelscope/ms-swift: Delivered broad model integration, stability improvements, and documentation enhancements that collectively expand model compatibility, accelerate experimentation, and improve training reliability. Highlights include multi-model support, improved training configurability, and targeted fixes across distributed training, packing, and data handling.
April 2025
57 Commits • 22 Features
Apr 1, 2025April 2025 monthly summary for modelscope/ms-swift. Focused on expanding quantization workflows, model packing efficiency, and deployment readiness, while continuing to harden data handling and developer experience. Key outcomes include: (1) broadening quantization and Omni support for Qwen ecosystems; (2) adding internvl2 packing workflow improvements; (3) introducing MOE quantization paths; (4) upgrading the Liger kernel with LLama4, adding a dedicated Swift Docker image, and enabling streaming shuffle; (5) expanding Qwen3/Qwen2/Qwen2.5 Omni-3B and related MoE/self-cognition features to the ecosystem, plus ongoing documentation updates and bug fixes that stabilize runtime behavior.
57 Commits • 22 Features
Apr 1, 2025April 2025 monthly summary for modelscope/ms-swift. Focused on expanding quantization workflows, model packing efficiency, and deployment readiness, while continuing to harden data handling and developer experience. Key outcomes include: (1) broadening quantization and Omni support for Qwen ecosystems; (2) adding internvl2 packing workflow improvements; (3) introducing MOE quantization paths; (4) upgrading the Liger kernel with LLama4, adding a dedicated Swift Docker image, and enabling streaming shuffle; (5) expanding Qwen3/Qwen2/Qwen2.5 Omni-3B and related MoE/self-cognition features to the ecosystem, plus ongoing documentation updates and bug fixes that stabilize runtime behavior.
April 2025
March 2025
85 Commits • 25 Features
Mar 1, 2025March 2025 highlights: Delivered broad backend compatibility, expanded multimodal and engine capabilities, and stabilized core runtime for scalable deployments. Strengthened Megatron and Qwen/Qwen2.5 VL support, improved tokenizer handling, and refined release CI/docs to accelerate safe, enterprise-grade rollouts.
March 2025
85 Commits • 25 Features
Mar 1, 2025March 2025 highlights: Delivered broad backend compatibility, expanded multimodal and engine capabilities, and stabilized core runtime for scalable deployments. Strengthened Megatron and Qwen/Qwen2.5 VL support, improved tokenizer handling, and refined release CI/docs to accelerate safe, enterprise-grade rollouts.
February 2025
73 Commits • 28 Features
Feb 1, 2025February 2025 performance snapshot for modelscope/ms-swift. Focused on stabilizing production grounding and deployment, expanding inference metrics and model support, and strengthening GRPO reliability and ecosystem integrations. Delivered concrete features and fixes that improve model evaluation, deployment reliability, and time-to-value for customers.
73 Commits • 28 Features
Feb 1, 2025February 2025 performance snapshot for modelscope/ms-swift. Focused on stabilizing production grounding and deployment, expanding inference metrics and model support, and strengthening GRPO reliability and ecosystem integrations. Delivered concrete features and fixes that improve model evaluation, deployment reliability, and time-to-value for customers.
February 2025
January 2025
61 Commits • 22 Features
Jan 1, 2025Month: 2025-01 — The ms-swift team delivered a focused set of stability improvements, feature enablement, and expanded model support that together improve reliability, practitioner productivity, and production readiness. Key work included a sweeping core stability and IO fixes across the engine (suffix handling, padding, initialization, file naming/cache lookups, templates, and writers), enabling more stable inference pipelines and data I/O. We added reward modeling support with quant-bert reward behavior and training workflows to accelerate RLHF experiments. The model catalog and user demos were refreshed to reflect current capabilities, and tooling shells were refined for better interoperability. Hardware acceleration and cross-platform effort advanced with MPS support for macOS, updates to the base_to_chat shell, and PPO compatibility, expanding deployment options. Finally, DeepSeek-R1 integration and related distillation tooling were completed, alongside ongoing maintenance improvements (documentation fixes and dependency updates) to reduce risk and speed developer throughput.
January 2025
61 Commits • 22 Features
Jan 1, 2025Month: 2025-01 — The ms-swift team delivered a focused set of stability improvements, feature enablement, and expanded model support that together improve reliability, practitioner productivity, and production readiness. Key work included a sweeping core stability and IO fixes across the engine (suffix handling, padding, initialization, file naming/cache lookups, templates, and writers), enabling more stable inference pipelines and data I/O. We added reward modeling support with quant-bert reward behavior and training workflows to accelerate RLHF experiments. The model catalog and user demos were refreshed to reflect current capabilities, and tooling shells were refined for better interoperability. Hardware acceleration and cross-platform effort advanced with MPS support for macOS, updates to the base_to_chat shell, and PPO compatibility, expanding deployment options. Finally, DeepSeek-R1 integration and related distillation tooling were completed, alongside ongoing maintenance improvements (documentation fixes and dependency updates) to reduce risk and speed developer throughput.
December 2024
101 Commits • 28 Features
Dec 1, 2024December 2024 highlights for modelscope/ms-swift: delivered substantial feature expansion, reliability improvements, and business value through expanded model compatibility, notebook tooling enhancements, and robust deployment/documentation updates. Key initiatives included refactoring MLLM and enabling Telechat2, expanding support for llama3.3, internvl2.5, and DeepSeek variants, and strengthening core integrations (adapters, Megrez Omni, Qwen branding, WeChat, UI banner). Also advanced LLM notebook tooling, updated inference/deploy/export examples, and added image mapping. Major bug fixes improved context handling, streaming stability, dataset loading, and web UI reliability, while documentation and examples were modernized to speed onboarding. The month demonstrates solid full‑stack capabilities—code quality, testing, docs, and cross‑repo collaboration—driving faster time‑to‑value for customers and broader model coverage.
101 Commits • 28 Features
Dec 1, 2024December 2024 highlights for modelscope/ms-swift: delivered substantial feature expansion, reliability improvements, and business value through expanded model compatibility, notebook tooling enhancements, and robust deployment/documentation updates. Key initiatives included refactoring MLLM and enabling Telechat2, expanding support for llama3.3, internvl2.5, and DeepSeek variants, and strengthening core integrations (adapters, Megrez Omni, Qwen branding, WeChat, UI banner). Also advanced LLM notebook tooling, updated inference/deploy/export examples, and added image mapping. Major bug fixes improved context handling, streaming stability, dataset loading, and web UI reliability, while documentation and examples were modernized to speed onboarding. The month demonstrates solid full‑stack capabilities—code quality, testing, docs, and cross‑repo collaboration—driving faster time‑to‑value for customers and broader model coverage.
December 2024
November 2024
30 Commits • 4 Features
Nov 1, 2024November 2024 (month: 2024-11) – ms-swift focus was on expanding model coverage, stabilizing deployment pipelines, and strengthening cross-cutting compatibility to accelerate time-to-value for customers. Key features delivered include expanded model support and deployment readiness, while major bug fixes addressed deployment reliability, quantization, and preprocessing/evaluation stability. Overall, the work increased model throughput, reduced downtime, and improved developer experience across the end-to-end inference and deployment workflow.
November 2024
30 Commits • 4 Features
Nov 1, 2024November 2024 (month: 2024-11) – ms-swift focus was on expanding model coverage, stabilizing deployment pipelines, and strengthening cross-cutting compatibility to accelerate time-to-value for customers. Key features delivered include expanded model support and deployment readiness, while major bug fixes addressed deployment reliability, quantization, and preprocessing/evaluation stability. Overall, the work increased model throughput, reduced downtime, and improved developer experience across the end-to-end inference and deployment workflow.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 performance summary for repository modelscope/ms-swift. Delivered critical feature enhancements and fixed core training/evaluation issues, improving evaluation reliability and training accuracy. Key outcomes include support for a new model type longwriter_glm4_9b, stabilization of evaluation handling for past_key_values in internvl2, and padding-aware loss calculation in Seq2SeqTrainer for transformers v4.46+. These changes improve model deployment readiness and product reliability, reducing debugging effort and enabling broader model experimentation. Strengthened docs to reflect transformer version requirements and feature adjustments.
2 Commits • 1 Features
Oct 1, 2024October 2024 performance summary for repository modelscope/ms-swift. Delivered critical feature enhancements and fixed core training/evaluation issues, improving evaluation reliability and training accuracy. Key outcomes include support for a new model type longwriter_glm4_9b, stabilization of evaluation handling for past_key_values in internvl2, and padding-aware loss calculation in Seq2SeqTrainer for transformers v4.46+. These changes improve model deployment readiness and product reliability, reducing debugging effort and enabling broader model experimentation. Strengthened docs to reflect transformer version requirements and feature adjustments.
October 2024
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Quality Metrics
Correctness86.0%
Maintainability85.2%
Architecture83.0%
Performance74.8%
AI Usage20.6%
Skills & Technologies
Programming Languages
Jinja2Jupyter NotebookMarkdownPyTorchPythonRSTShellTextTorchYAML
Technical Skills
API DesignAPI DevelopmentAPI IntegrationAWQAdapter LoadingAdapter TrainingAdapter TuningAdapter-based Fine-tuningAdapter-based trainingAgent DevelopmentApple SiliconArgument ParsingAsyncIOAsynchronous ProgrammingAsyncio
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