--- title: "Awesome-Multimodal-Large-Language-Models vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-declare-lab-instruct-eval" tools: ["bradyfu-awesome-multimodal-large-language-models", "declare-lab-instruct-eval"] --- # Awesome-Multimodal-Large-Language-Models vs instruct-eval *GraphCanon updated Jul 12, 2026* ## Verdict Pick Awesome-Multimodal-Large-Language-Models when tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-tuning, multi-modality; pick instruct-eval when tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. [Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) reports 18k GitHub stars, 1.1k forks, and 104 open issues, last pushed Jul 2, 2026. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [Awesome-Multimodal-Large-Language-Models's repository](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | Latest Advances on Multimodal Large Language Models | Code for evaluating instruction-tuned language models like Alpaca and Flan-T5 | | Stars | 17,937 | 552 | | Forks | 1,129 | 45 | | Open issues | 104 | 24 | | Language | - | Python | | Adopt for | Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking | - | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 8d | 853d | | Open issues (now) | 104 | 24 | | Owner type | User | Organization | | Security scan | No lockfile | 83 low (83 low) | | Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/declare-lab-instruct-eval/trust.md) | ## Decision facts: Awesome-Multimodal-Large-Language-Models - **Adopt for:** Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking ## Choose when ### Choose Awesome-Multimodal-Large-Language-Models if… - Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-tuning, multi-modality. - Also covers LLM Frameworks. - - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. ### Choose instruct-eval if… - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. - Leaner open-issue backlog (24). ## When NOT to use Awesome-Multimodal-Large-Language-Models - - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ## When NOT to use instruct-eval - Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between Awesome-Multimodal-Large-Language-Models and instruct-eval? Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal Large Language Models. instruct-eval: Code for evaluating instruction-tuned language models like Alpaca and Flan-T5. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Multimodal-Large-Language-Models over instruct-eval? Choose Awesome-Multimodal-Large-Language-Models over instruct-eval when Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-tuning, multi-modality; Also covers LLM Frameworks; - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. ### When should I choose instruct-eval over Awesome-Multimodal-Large-Language-Models? Choose instruct-eval over Awesome-Multimodal-Large-Language-Models when Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; Leaner open-issue backlog (24). ### When should I avoid Awesome-Multimodal-Large-Language-Models? - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ### When should I avoid instruct-eval? Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is Awesome-Multimodal-Large-Language-Models or instruct-eval more popular on GitHub? Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,937 vs 552). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Multimodal-Large-Language-Models and instruct-eval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or instruct-eval? GraphCanon lists graph-backed alternatives at [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/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/bradyfu-awesome-multimodal-large-language-models-vs-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Multimodal-Large-Language-Models or instruct-eval? Awesome-Multimodal-Large-Language-Models: Active. instruct-eval: 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 Awesome-Multimodal-Large-Language-Models and instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Multimodal-Large-Language-Models trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models`](/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models) - 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/_