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Comparison

MiniMax-01 vs Chinese-LLaMA-Alpaca-2

Verdict

Pick MiniMax-01 if miniMax-01 optimizes Linear Attention for large-language and vision-language models; pick Chinese-LLaMA-Alpaca-2 if chinese-LLaMA-Alpaca-2 provides Chinese versions of LLaMA-2 and Alpaca-2 large language models, including long context models up to 64K. It includes pretraining and instruction fine-tuning scripts, and supports various部署.

Markdown twin · MiniMax-01 alternatives · Chinese-LLaMA-Alpaca-2 alternatives

GraphCanon updated Sep 20, 2026

9views this month

MiniMax-01 logo

MiniMax-01

MiniMax-AI/MiniMax-01

★ 3.5kpushed Jul 7, 2025
vs
Chinese-LLaMA-Alpaca-2 logo

Chinese-LLaMA-Alpaca-2

ymcui/Chinese-LLaMA-Alpaca-2

★ 7.1kpushed Apr 19, 2026

Trust & integrity

SignalMiniMax-01Chinese-LLaMA-Alpaca-2
Maintenance
Dormant (406d since push)
As of Aug 18, 2026 · github_public_v1
Slowing (152d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Aug 18, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
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
MiniMax-01 trust reportChinese-LLaMA-Alpaca-2 trust reportTrust methodology

Tagline

MiniMax-01
Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention
Chinese-LLaMA-Alpaca-2
Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models

Stars

MiniMax-01
3.5k
Chinese-LLaMA-Alpaca-2
7.1k

Forks

MiniMax-01
332
Chinese-LLaMA-Alpaca-2
559

Open issues

MiniMax-01
8
Chinese-LLaMA-Alpaca-2
6

Language

MiniMax-01
Python
Chinese-LLaMA-Alpaca-2
Python

Adopt for

MiniMax-01
MiniMax-01 optimizes Linear Attention for large-language and vision-language models.
Chinese-LLaMA-Alpaca-2
Chinese-LLaMA-Alpaca-2 provides Chinese versions of LLaMA-2 and Alpaca-2 large language models, including long context models up to 64K. It includes pretraining and instruction fine-tuning scripts, and supports various部署

Persona

MiniMax-01
-
Chinese-LLaMA-Alpaca-2
-

Runtime

MiniMax-01
-
Chinese-LLaMA-Alpaca-2
-

License

MiniMax-01
MIT
Chinese-LLaMA-Alpaca-2
Apache-2.0

Last pushed

MiniMax-01
Jul 7, 2025
Chinese-LLaMA-Alpaca-2
Apr 19, 2026

Categories

MiniMax-01
LLM Frameworks, Model Training
Chinese-LLaMA-Alpaca-2
Inference & Serving, LLM Frameworks, Model Training

Trust and health

Maintenance

MiniMax-01
Dormant (18%)
Chinese-LLaMA-Alpaca-2
Slowing (36%)

Days since push

MiniMax-01
406d
Chinese-LLaMA-Alpaca-2
152d

Open issues (now)

MiniMax-01
8
Chinese-LLaMA-Alpaca-2
6

Stars delta

MiniMax-01
+17 (30d)
Chinese-LLaMA-Alpaca-2
-6 (30d)

Owner type

MiniMax-01
Organization
Chinese-LLaMA-Alpaca-2
User

OSV dependency advisories

MiniMax-01
No lockfile (source not queried)
Chinese-LLaMA-Alpaca-2
Published findings

Full report

MiniMax-01
Trust report
Chinese-LLaMA-Alpaca-2
Trust report

Choose MiniMax-01 if…

  • License: MiniMax-01 is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0.
  • Tags unique to MiniMax-01: large-language-models, llm, vision-language-model, vlm.
  • When high throughput performance is required for model serving

When NOT to use MiniMax-01

  • If deep customization of attention mechanisms aside from Linear Attention is needed
  • In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility
GitHubDetails

Choose Chinese-LLaMA-Alpaca-2 if…

  • License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, MiniMax-01 is MIT.
  • Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention.
  • Also covers Inference & Serving.
  • When you need Chinese-specific large language models with long context capabilities up to 64K.

When NOT to use Chinese-LLaMA-Alpaca-2

  • If you need models that support languages other than Chinese, as this repository focuses on Chinese models.
  • If you require models with shorter context lengths than 4K, as the standard models support 4K and the long context models support 16K and 64K.
  • If you are looking for models that do not include human preference alignment (RLHF), as this repository includes models that have been fine-tuned for this purpose.
GitHubDetails

Explore

MiniMax-01 trust report →Chinese-LLaMA-Alpaca-2 trust report →LLM Frameworks category →Model Training category →Inference & Serving 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: MiniMax-01 3.5k · Chinese-LLaMA-Alpaca-2 7.1k (synced Sep 20, 2026).

Common questions

What is the difference between MiniMax-01 and Chinese-LLaMA-Alpaca-2?
MiniMax-01: Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention. Chinese-LLaMA-Alpaca-2: Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models. See the comparison table for live GitHub stats and shared categories.
When should I choose MiniMax-01 over Chinese-LLaMA-Alpaca-2?
Choose MiniMax-01 over Chinese-LLaMA-Alpaca-2 when License: MiniMax-01 is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0; Tags unique to MiniMax-01: large-language-models, llm, vision-language-model, vlm; When high throughput performance is required for model serving.
When should I choose Chinese-LLaMA-Alpaca-2 over MiniMax-01?
Choose Chinese-LLaMA-Alpaca-2 over MiniMax-01 when License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, MiniMax-01 is MIT; Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention; Also covers Inference & Serving; When you need Chinese-specific large language models with long context capabilities up to 64K.
When should I avoid MiniMax-01?
If deep customization of attention mechanisms aside from Linear Attention is needed In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility
When should I avoid Chinese-LLaMA-Alpaca-2?
If you need models that support languages other than Chinese, as this repository focuses on Chinese models. If you require models with shorter context lengths than 4K, as the standard models support 4K and the long context models support 16K and 64K. If you are looking for models that do not include human preference alignment (RLHF), as this repository includes models that have been fine-tuned for this purpose.
Is MiniMax-01 or Chinese-LLaMA-Alpaca-2 more popular on GitHub?
Chinese-LLaMA-Alpaca-2 has more GitHub stars (7,118 vs 3,463). Stars measure visibility, not whether either tool fits your constraints.
Are MiniMax-01 and Chinese-LLaMA-Alpaca-2 open source?
Yes - both are open-source projects on GitHub (MiniMax-01: MIT, Chinese-LLaMA-Alpaca-2: Apache-2.0).
Where can I find alternatives to MiniMax-01 or Chinese-LLaMA-Alpaca-2?
GraphCanon lists graph-backed alternatives at MiniMax-01 alternatives and Chinese-LLaMA-Alpaca-2 alternatives (MiniMax-01 markdown twin, Chinese-LLaMA-Alpaca-2 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, MiniMax-01 or Chinese-LLaMA-Alpaca-2?
MiniMax-01: Dormant. Chinese-LLaMA-Alpaca-2: Slowing. 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 MiniMax-01 and Chinese-LLaMA-Alpaca-2?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: MiniMax-01 trust report; Chinese-LLaMA-Alpaca-2 trust report.

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