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Comparison

Yi vs llm

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 llm if decision-critical facts for 'llm'.

Markdown twin · Yi alternatives · llm alternatives

GraphCanon updated Sep 30, 2026

8views this month

Yi logo

Yi

01-ai/Yi

★ 7.8kpushed Nov 27, 2024
vs
llm logo

llm

simonw/llm

★ 12kpushed Sep 2, 2026

Trust & integrity

SignalYillm
Maintenance
Dormant (659d since push)
As of Sep 18, 2026 · github_public_v1
Very active (4d since push)
As of Sep 7, 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 7, 2026 · github_public_v1
OSV dependency advisories
Published findings
As of Sep 18, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 11, 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
Yi trust reportllm trust reportTrust methodology

Tagline

Yi
A series of large language models trained from scratch
llm
Access large language models from the command-line

Stars

Yi
7.8k
llm
12k

Forks

Yi
499
llm
978

Open issues

Yi
31
llm
689

Language

Yi
Jupyter Notebook
llm
Python

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
llm
Decision-critical facts for 'llm'

Persona

Yi
-
llm
-

Runtime

Yi
-
llm
-

License

Yi
Apache-2.0
llm
Apache-2.0

Last pushed

Yi
Nov 27, 2024
llm
Sep 2, 2026

Categories

Yi
Inference & Serving, LLM Frameworks
llm
Inference & Serving, LLM Frameworks

Trust and health

Maintenance

Yi
Dormant (18%)
llm
Very active (96%)

Days since push

Yi
659d
llm
4d

Open issues (now)

Yi
31
llm
689

Stars delta

Yi
+11 (30d)
llm
+149 (30d)

Open issues delta

Yi
0 (30d)
llm
+25 (30d)

Owner type

Yi
Organization
llm
User

OSV dependency advisories

Yi
Published findings
llm
No lockfile (source not queried)

Full report

Yi
Trust report
llm
Trust report

Shared compatibility

  • Python · Yi: Python runtime · llm: Python runtime

Choose Yi if…

  • Yi is primarily Jupyter Notebook; llm is Python.
  • 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, large-language-models, local-deployment.
  • 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.
GitHubDetails

Choose llm if…

  • llm is primarily Python; Yi is Jupyter Notebook.
  • Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities..
  • Tags unique to llm: ai, llms, openai.
  • - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods.

When NOT to use llm

  • - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based.
  • - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models.
GitHubDetails

Explore

Yi trust report →llm trust report →Inference & Serving category →LLM Frameworks 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 on cards: Yi 7.8k · llm 12k (synced Sep 20, 2026).

Common questions

What is the difference between Yi and llm?
Yi: A series of large language models trained from scratch. llm: Access large language models from the command-line. See the comparison table for live GitHub stats and shared categories.
When should I choose Yi over llm?
Choose Yi over llm when Yi is primarily Jupyter Notebook; llm is Python; 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, large-language-models, local-deployment; 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 llm over Yi?
Choose llm over Yi when llm is primarily Python; Yi is Jupyter Notebook; Requirements: - Installation supports multiple methods including pip, Homebrew (with caveats noted), pipx, and uv.; - Requires an OpenAI API key for certain functionalities.; Tags unique to llm: ai, llms, openai; - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods.
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 llm?
- If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models.
Is Yi or llm more popular on GitHub?
llm has more GitHub stars (12,473 vs 7,833). Stars measure visibility, not whether either tool fits your constraints.
Are Yi and llm open source?
Yes - both are open-source projects on GitHub (Yi: Apache-2.0, llm: Apache-2.0).
Where can I find alternatives to Yi or llm?
GraphCanon lists graph-backed alternatives at Yi alternatives and llm alternatives (Yi markdown twin, llm 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 llm?
Yi: Dormant. llm: 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 llm?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Yi trust report; llm trust report.

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