Comparison
long-context-attention vs Chinese-LLaMA-Alpaca-2
Verdict
Pick long-context-attention if long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference; 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 · long-context-attention alternatives · Chinese-LLaMA-Alpaca-2 alternatives
GraphCanon updated Sep 20, 2026
5views this month
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Trust & integrity
| Signal | long-context-attention | Chinese-LLaMA-Alpaca-2 |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (152d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 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 |
Tagline
- long-context-attention
- Unified Sequence Parallel Attention for Long Context Transformers
- Chinese-LLaMA-Alpaca-2
- Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models
Stars
- long-context-attention
- 693
- Chinese-LLaMA-Alpaca-2
- 7.1k
Forks
- long-context-attention
- 85
- Chinese-LLaMA-Alpaca-2
- 559
Open issues
- long-context-attention
- 13
- Chinese-LLaMA-Alpaca-2
- 6
Language
- long-context-attention
- Python
- Chinese-LLaMA-Alpaca-2
- Python
Adopt for
- long-context-attention
- long-context-attention uses Unified Sequence Parallel Attention techniques to improve performance of long context transformers for both training and inference.
- 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
- long-context-attention
- -
- Chinese-LLaMA-Alpaca-2
- -
Runtime
- long-context-attention
- -
- Chinese-LLaMA-Alpaca-2
- -
License
- long-context-attention
- Apache-2.0
- Chinese-LLaMA-Alpaca-2
- Apache-2.0
Last pushed
- long-context-attention
- Sep 16, 2026
- Chinese-LLaMA-Alpaca-2
- Apr 19, 2026
Categories
- long-context-attention
- Inference & Serving, Model Training
- Chinese-LLaMA-Alpaca-2
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- long-context-attention
- Very active (96%)
- Chinese-LLaMA-Alpaca-2
- Slowing (36%)
Days since push
- long-context-attention
- 3d
- Chinese-LLaMA-Alpaca-2
- 152d
Open issues (now)
- long-context-attention
- 13
- Chinese-LLaMA-Alpaca-2
- 6
Stars delta
- long-context-attention
- +11 (30d)
- Chinese-LLaMA-Alpaca-2
- -6 (30d)
OSV dependency advisories
- long-context-attention
- No lockfile (source not queried)
- Chinese-LLaMA-Alpaca-2
- Published findings
Full report
- long-context-attention
- Trust report
- Chinese-LLaMA-Alpaca-2
- Trust report
Choose long-context-attention if…
- Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training.
- When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues.
- More recently updated (last pushed Sep 16, 2026).
When NOT to use long-context-attention
- If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits.
- When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.
Choose Chinese-LLaMA-Alpaca-2 if…
- Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention.
- Also covers LLM Frameworks.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (feifeibear/long-context-attention) · observed Sep 20, 2026
- GitHub forks (feifeibear/long-context-attention) · observed Sep 20, 2026
- Last push (feifeibear/long-context-attention) · observed Sep 16, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ymcui/Chinese-LLaMA-Alpaca-2) · observed Sep 20, 2026
- GitHub forks (ymcui/Chinese-LLaMA-Alpaca-2) · observed Sep 20, 2026
- Last push (ymcui/Chinese-LLaMA-Alpaca-2) · observed Apr 19, 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: long-context-attention 693 · Chinese-LLaMA-Alpaca-2 7.1k (synced Sep 20, 2026).
Common questions
- What is the difference between long-context-attention and Chinese-LLaMA-Alpaca-2?
- long-context-attention: Unified Sequence Parallel Attention for Long Context Transformers. 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 long-context-attention over Chinese-LLaMA-Alpaca-2?
- Choose long-context-attention over Chinese-LLaMA-Alpaca-2 when Tags unique to long-context-attention: attention-is-all-you-need, deepspeed-ulysses, llm-inference, llm-training; When developing models that require handling longer input sequences where traditional attention mechanisms face scalability issues; More recently updated (last pushed Sep 16, 2026).
- When should I choose Chinese-LLaMA-Alpaca-2 over long-context-attention?
- Choose Chinese-LLaMA-Alpaca-2 over long-context-attention when Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention; Also covers LLM Frameworks; When you need Chinese-specific large language models with long context capabilities up to 64K.
- When should I avoid long-context-attention?
- If your use case involves short context lengths where standard attention mechanisms suffice and adding long-context-attention doesn't provide significant benefits. When working in environments that do not support Python, as this tool is specifically developed for the Python ecosystem.
- 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 long-context-attention or Chinese-LLaMA-Alpaca-2 more popular on GitHub?
- Chinese-LLaMA-Alpaca-2 has more GitHub stars (7,118 vs 693). Stars measure visibility, not whether either tool fits your constraints.
- Are long-context-attention and Chinese-LLaMA-Alpaca-2 open source?
- Yes - both are open-source projects on GitHub (long-context-attention: Apache-2.0, Chinese-LLaMA-Alpaca-2: Apache-2.0).
- Where can I find alternatives to long-context-attention or Chinese-LLaMA-Alpaca-2?
- GraphCanon lists graph-backed alternatives at long-context-attention alternatives and Chinese-LLaMA-Alpaca-2 alternatives (long-context-attention 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, long-context-attention or Chinese-LLaMA-Alpaca-2?
- long-context-attention: Very active. 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 long-context-attention and Chinese-LLaMA-Alpaca-2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: long-context-attention trust report; Chinese-LLaMA-Alpaca-2 trust report.