Comparison
awesome-llms-fine-tuning vs gorilla
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.
Markdown twin · awesome-llms-fine-tuning alternatives · gorilla alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | awesome-llms-fine-tuning | gorilla |
|---|---|---|
| Maintenance | Dormant (599d since push) As of 3w · github_public_v1 | Slowing (117d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- 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)
Stars
- awesome-llms-fine-tuning
- 525
- gorilla
- 13k
Forks
- awesome-llms-fine-tuning
- 78
- gorilla
- 1.4k
Open issues
- awesome-llms-fine-tuning
- 9
- gorilla
- 272
Language
- awesome-llms-fine-tuning
- -
- gorilla
- Python
Adopt for
- awesome-llms-fine-tuning
- A curated list for LLM fine-tuning resources including tutorials, papers, and tools.
- gorilla
- Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
Persona
- awesome-llms-fine-tuning
- -
- gorilla
- -
Runtime
- awesome-llms-fine-tuning
- -
- gorilla
- -
License
- awesome-llms-fine-tuning
- (unknown) - (unknown)
- gorilla
- Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes.
Last pushed
- awesome-llms-fine-tuning
- Dec 2, 2024
- gorilla
- Apr 13, 2026
Categories
- awesome-llms-fine-tuning
- LLM Frameworks, Model Training
- gorilla
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- awesome-llms-fine-tuning
- Dormant (18%)
- gorilla
- Slowing (36%)
Days since push
- awesome-llms-fine-tuning
- 599d
- gorilla
- 117d
Open issues (now)
- awesome-llms-fine-tuning
- 9
- gorilla
- 272
Owner type
- awesome-llms-fine-tuning
- Organization
- gorilla
- User
Full report
- awesome-llms-fine-tuning
- Trust report
- gorilla
- Trust report
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 25, 2026
- GitHub forks (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Jul 25, 2026
- Last push (Curated-Awesome-Lists/awesome-llms-fine-tuning) · observed Dec 2, 2024
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ShishirPatil/gorilla) · observed Aug 8, 2026
- GitHub forks (ShishirPatil/gorilla) · observed Aug 8, 2026
- Last push (ShishirPatil/gorilla) · observed Apr 13, 2026
- License file (Apache-2.0) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-llms-fine-tuning 525 · gorilla 13k (synced Jul 25, 2026).
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 and gorilla alternatives (awesome-llms-fine-tuning markdown twin, gorilla 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, 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; gorilla trust report.