--- title: "transformers vs BELLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-transformers-vs-lianjiatech-belle" tools: ["huggingface-transformers", "lianjiatech-belle"] --- # transformers vs BELLE *GraphCanon updated Aug 16, 2026* ## Verdict Pick transformers if transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3; pick BELLE if bELLE is an open-source Chinese dialogue large model that focuses on improving instruction-following capabilities through fine-tuned pre-training models. [transformers](https://huggingface.co/transformers) reports 164k GitHub stars, 34k forks, and 2.4k open issues, last pushed Aug 15, 2026. [BELLE](https://github.com/LianjiaTech/BELLE) has 8.3k stars, 758 forks, and 106 open issues, last pushed Oct 16, 2024. Figures are from public GitHub metadata via [transformers's repository](https://github.com/huggingface/transformers) and [BELLE's repository](https://github.com/LianjiaTech/BELLE). | | [transformers](/tools/huggingface-transformers.md) | [BELLE](/tools/lianjiatech-belle.md) | | --- | --- | --- | | Tagline | Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models | Open-source Chinese dialogue large model | | Stars | 164,121 | 8,280 | | Forks | 34,249 | 758 | | Open issues | 2,382 | 106 | | Language | Python | HTML | | Adopt for | Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3 | BELLE is an open-source Chinese dialogue large model that focuses on improving instruction-following capabilities through fine-tuned pre-training models. | | Persona | - | - | | Runtime | - | - | | License | Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems. | Available under the Apache-2.0 license. | | Categories | Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [transformers](/tools/huggingface-transformers.md) | [BELLE](/tools/lianjiatech-belle.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 654d | | Open issues (now) | 2.4k | 106 | | Stars delta | +1.5k (30d) | Unknown | | Open issues delta | -97 (30d) | Unknown | | Full report | [trust report](/tools/huggingface-transformers/trust.md) | [trust report](/tools/lianjiatech-belle/trust.md) | ## Decision facts: transformers - **Requirements:** Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+ - **Adopt for:** Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3 - **License detail:** Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems. ## Decision facts: BELLE - **Hosting:** harness plugin - BELLE models are hosted on Hugging Face, contributing to a growing ecosystem of open-source language models. - **Pricing:** freemium - Free for non-commercial use under the Apache 2.0 license; commercial projects should review compliance or seek additional licensing options as needed. - **Adopt for:** BELLE is an open-source Chinese dialogue large model that focuses on improving instruction-following capabilities through fine-tuned pre-training models. - **License detail:** Available under the Apache-2.0 license. ## Choose when ### Choose transformers if… - transformers is primarily Python; BELLE is HTML. - Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+. - Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing. - Also covers Computer Vision, Speech & Audio. - The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs. ### Choose BELLE if… - BELLE is primarily HTML; transformers is Python. - BELLE models are hosted on Hugging Face, contributing to a growing ecosystem of open-source language models. - Pricing: Free for non-commercial use under the Apache 2.0 license; commercial projects should review compliance or seek additional licensing options as needed.. - Tags unique to BELLE: chinese-nlp, gpt-evaluation, instruct-finetune, llama. - When you need a specifically optimized language model for the Chinese language with enhanced dialogue capabilities. ## When NOT to use transformers - If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. - It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10. ## When NOT to use BELLE - When the project requires a multi-lingual model or extensive support for languages other than Chinese. - For applications that do not need fine-tuned instruction-following capabilities, since BELLE is particularly optimized for this aspect using data from ChatGPT exclusively. - If real-time voice recognition speed is critical and a slight delay can be tolerated, as alternatives may offer more balanced performance. ## Common questions ### What is the difference between transformers and BELLE? transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. BELLE: Open-source Chinese dialogue large model. See the comparison table for live GitHub stats and shared categories. ### When should I choose transformers over BELLE? Choose transformers over BELLE when transformers is primarily Python; BELLE is HTML; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing; Also covers Computer Vision, Speech & Audio; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs. ### When should I choose BELLE over transformers? Choose BELLE over transformers when BELLE is primarily HTML; transformers is Python; BELLE models are hosted on Hugging Face, contributing to a growing ecosystem of open-source language models; Pricing: Free for non-commercial use under the Apache 2.0 license; commercial projects should review compliance or seek additional licensing options as needed.; Tags unique to BELLE: chinese-nlp, gpt-evaluation, instruct-finetune, llama; When you need a specifically optimized language model for the Chinese language with enhanced dialogue capabilities. ### When should I avoid transformers? If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10. ### When should I avoid BELLE? When the project requires a multi-lingual model or extensive support for languages other than Chinese. For applications that do not need fine-tuned instruction-following capabilities, since BELLE is particularly optimized for this aspect using data from ChatGPT exclusively. If real-time voice recognition speed is critical and a slight delay can be tolerated, as alternatives may offer more balanced performance. ### Is transformers or BELLE more popular on GitHub? transformers has more GitHub stars (164,121 vs 8,280). Stars measure visibility, not whether either tool fits your constraints. ### Are transformers and BELLE open source? Yes - both are open-source projects on GitHub (transformers: Apache-2.0, BELLE: Apache-2.0). ### Where can I find alternatives to transformers or BELLE? GraphCanon lists graph-backed alternatives at [transformers alternatives](/tools/huggingface-transformers/alternatives) and [BELLE alternatives](/tools/lianjiatech-belle/alternatives) ([transformers markdown twin](/tools/huggingface-transformers/alternatives.md), [BELLE markdown twin](/tools/lianjiatech-belle/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/huggingface-transformers-vs-lianjiatech-belle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, transformers or BELLE? transformers: Very active. BELLE: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for transformers and BELLE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [transformers trust report](/tools/huggingface-transformers/trust); [BELLE trust report](/tools/lianjiatech-belle/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-transformers`](/api/graphcanon/graph?tool=huggingface-transformers) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_