--- title: "awesome-llms-fine-tuning vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/curated-awesome-lists-awesome-llms-fine-tuning-vs-shishirpatil-gorilla" tools: ["curated-awesome-lists-awesome-llms-fine-tuning", "shishirpatil-gorilla"] --- # awesome-llms-fine-tuning vs gorilla *GraphCanon updated Aug 24, 2026* ## Verdict Pick awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) reports 525 GitHub stars, 79 forks, and 10 open issues, last pushed Dec 2, 2024. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 272 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [awesome-llms-fine-tuning's repository](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | A comprehensive collection of resources for fine-tuning Large Language Models. | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 525 | 12,988 | | Forks | 79 | 1,397 | | Open issues | 10 | 272 | | Language | - | Python | | Adopt for | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | (unknown) - (unknown) | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 629d | 117d | | Open issues (now) | 10 | 272 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: awesome-llms-fine-tuning - **Adopt for:** A curated list for LLM fine-tuning resources including tutorials, papers, and tools. - **License detail:** (unknown) - (unknown) ## Decision facts: gorilla - **Pricing:** freemium - **Requirements:** Gorilla works best with Python environments and requires installation through pip or local repository cloning. - **Adopt for:** Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. - **License detail:** Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. ## Choose when ### Choose awesome-llms-fine-tuning if… - Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning. - Also covers LLM Frameworks. - Need extensive guidance on LLM-specific fine-tuning strategies ### Choose gorilla if… - Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.. - Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api. - Also covers Evaluation & Observability. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ## When NOT to use awesome-llms-fine-tuning - Looking for real-time interactive support or direct code implementation help - Favor more specialized tools for immediate performance optimization over broad learning ## When NOT to use gorilla - Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. - If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ## Common questions ### What is the difference between awesome-llms-fine-tuning and gorilla? awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-llms-fine-tuning over gorilla? Choose awesome-llms-fine-tuning over gorilla when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, fine-tuning; Also covers LLM Frameworks; Need extensive guidance on LLM-specific fine-tuning strategies. ### When should I choose gorilla over awesome-llms-fine-tuning? Choose gorilla over awesome-llms-fine-tuning when Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api; Also covers Evaluation & Observability; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### When should I avoid awesome-llms-fine-tuning? Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning ### When should I avoid gorilla? Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ### Is awesome-llms-fine-tuning or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 525). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llms-fine-tuning and gorilla open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-llms-fine-tuning or gorilla? GraphCanon lists graph-backed alternatives at [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives.md), [gorilla markdown twin](/tools/shishirpatil-gorilla/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/curated-awesome-lists-awesome-llms-fine-tuning-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-llms-fine-tuning or gorilla? awesome-llms-fine-tuning: Dormant. gorilla: 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 awesome-llms-fine-tuning and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llms-fine-tuning trust report](/tools/curated-awesome-lists-awesome-llms-fine-tuning/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning`](/api/graphcanon/graph?tool=curated-awesome-lists-awesome-llms-fine-tuning) - 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/_