Comparison
Yi vs awesome-LLM-resources
Verdict
Pick Yi if yi is a series of large language models trained from scratch, offering local deployment and inference capabilities. It is suitable for users who need to run large language models locally and have the necessary hardware,如; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from.
Markdown twin · Yi alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 30, 2026
18views this month
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Trust & integrity
| Signal | Yi | awesome-LLM-resources |
|---|---|---|
| Maintenance | Dormant (659d since push) As of Sep 18, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Yi
- A series of large language models trained from scratch
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- Yi
- 7.8k
- awesome-LLM-resources
- 9.0k
Forks
- Yi
- 499
- awesome-LLM-resources
- 993
Open issues
- Yi
- 31
- awesome-LLM-resources
- 40
Language
- Yi
- Jupyter Notebook
- awesome-LLM-resources
- -
Adopt for
- Yi
- Yi is a series of large language models trained from scratch, offering local deployment and inference capabilities. It is suitable for users who need to run large language models locally and have the necessary hardware,如
- awesome-LLM-resources
- awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Persona
- Yi
- -
- awesome-LLM-resources
- -
Runtime
- Yi
- -
- awesome-LLM-resources
- -
License
- Yi
- Apache-2.0
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- Yi
- Nov 27, 2024
- awesome-LLM-resources
- Sep 14, 2026
Categories
- Yi
- Inference & Serving, LLM Frameworks
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- Yi
- Dormant (18%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- Yi
- 659d
- awesome-LLM-resources
- 3d
Open issues (now)
- Yi
- 31
- awesome-LLM-resources
- 40
Stars delta
- Yi
- +11 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- Yi
- 0 (30d)
- awesome-LLM-resources
- +17 (30d)
Owner type
- Yi
- Organization
- awesome-LLM-resources
- User
OSV dependency advisories
- Yi
- Published findings
- awesome-LLM-resources
- No lockfile (source not queried)
Full report
- awesome-LLM-resources
- Trust report
Choose Yi if…
- Requirements: Min 80 GB RAM; Ensure Python 3.10 or later is installed.; Hardware requirements vary depending on the specific Yi model being used..
- Tags unique to Yi: inference, jupyter-notebook, local-deployment, pip.
- Yi ships Docker support for self-hosted deployment.
- Use Yi if you require a large language model that can be deployed and run locally, especially on hardware like the A800 (80G).
When NOT to use Yi
- Avoid Yi if your hardware does not meet the minimum requirements for running the models, such as the A800 (80G) for Yi-34B-Chat.
- Do not use Yi if you prefer cloud-based solutions or do not have the capability to install and manage the local environment required for Yi.
Choose awesome-LLM-resources if…
- Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
- Requirements: The repository does not specify any technical requirements for accessing its content..
- Tags unique to awesome-LLM-resources: awesome-list, book, course, llama.
- Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Model Training.
- When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
When NOT to use awesome-LLM-resources
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
- When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Explore
Yi trust report →awesome-LLM-resources trust report →Inference & Serving category →LLM Frameworks category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (01-ai/Yi) · observed Sep 20, 2026
- GitHub forks (01-ai/Yi) · observed Sep 20, 2026
- Last push (01-ai/Yi) · observed Nov 27, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: Yi 7.8k · awesome-LLM-resources 9.0k (synced Sep 20, 2026).
Common questions
- What is the difference between Yi and awesome-LLM-resources?
- Yi: A series of large language models trained from scratch. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Yi over awesome-LLM-resources?
- Choose Yi over awesome-LLM-resources when Requirements: Min 80 GB RAM; Ensure Python 3.10 or later is installed.; Hardware requirements vary depending on the specific Yi model being used.; Tags unique to Yi: inference, jupyter-notebook, local-deployment, pip; Yi ships Docker support for self-hosted deployment; Use Yi if you require a large language model that can be deployed and run locally, especially on hardware like the A800 (80G).
- When should I choose awesome-LLM-resources over Yi?
- Choose awesome-LLM-resources over Yi when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- When should I avoid Yi?
- Avoid Yi if your hardware does not meet the minimum requirements for running the models, such as the A800 (80G) for Yi-34B-Chat. Do not use Yi if you prefer cloud-based solutions or do not have the capability to install and manage the local environment required for Yi.
- When should I avoid awesome-LLM-resources?
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
- Is Yi or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,968 vs 7,833). Stars measure visibility, not whether either tool fits your constraints.
- Are Yi and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (Yi: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to Yi or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at Yi alternatives and awesome-LLM-resources alternatives (Yi markdown twin, awesome-LLM-resources markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Yi or awesome-LLM-resources?
- Yi: Dormant. awesome-LLM-resources: Very active. 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 Yi and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Yi trust report; awesome-LLM-resources trust report.