REPOGEO REPORT · LITE
Troyanovsky/Local-LLM-Comparison-Colab-UI
Default branch main · commit 11572771 · scanned 6/23/2026, 8:13:11 PM
GitHub: 1,099 stars · 157 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Troyanovsky/Local-LLM-Comparison-Colab-UI, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition README opening to clarify its core purpose
Why:
CURRENT# Local LLM Comparison & Colab Links (WIP) (Update Nov. 27, 2023) The original goal of the repo was to compare some smaller models (7B and 13B) that can be run on consumer hardware so every model had a score for a set of questions from GPT-4. But I realized that as there are many more capable models appearing, the evaluation and comparison process may not suffice.
COPY-PASTE FIX# Local LLM Comparison & Colab Links (WIP) This repository provides a user-friendly Colab WebUI for interactively comparing the performance of various large language models (LLMs) that can be deployed locally on consumer hardware. Easily try out and assess different models firsthand to determine which best fits your specific needs.
- highlicense#2Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a LICENSE file in the root of the repository, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and adding the necessary license text.
- mediumtopics#3Update repository topics for better specificity
Why:
CURRENTai, gpt, llama, llm
COPY-PASTE FIXllm-comparison, local-llm, colab, webui, consumer-hardware, machine-learning, artificial-intelligence
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- Llama 3 (8B Instruct) · recommended 1×
- Mixtral 8x7B Instruct · recommended 1×
- Gemma (7B Instruct) · recommended 1×
- Mistral 7B Instruct v0.2 · recommended 1×
- Phi-3-mini (3.8B Instruct) · recommended 1×
- CATEGORY QUERYWhat are the best open-source LLMs to run locally on my consumer hardware?you: not recommendedAI recommended (in order):
- Llama 3 (8B Instruct)
- Mixtral 8x7B Instruct
- Gemma (7B Instruct)
- Mistral 7B Instruct v0.2
- Phi-3-mini (3.8B Instruct)
- Ollama (ollama/ollama)
- LM Studio
- Jan (janhq/jan)
- llama.cpp (ggerganov/llama.cpp)
AI recommended 9 alternatives but never named Troyanovsky/Local-LLM-Comparison-Colab-UI. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow can I easily evaluate various large language models for my specific application?you: not recommendedAI recommended (in order):
- LangChain
- Weights & Biases (W&B) Prompts
- Humanloop
- MLflow
- Galileo (by Arize AI)
- EleutherAI's LM Evaluation Harness
- Hugging Face Transformers
- Hugging Face Datasets
AI recommended 8 alternatives but never named Troyanovsky/Local-LLM-Comparison-Colab-UI. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of Troyanovsky/Local-LLM-Comparison-Colab-UI?passAI did not name Troyanovsky/Local-LLM-Comparison-Colab-UI — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts Troyanovsky/Local-LLM-Comparison-Colab-UI in production, what risks or prerequisites should they evaluate first?passAI did not name Troyanovsky/Local-LLM-Comparison-Colab-UI — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo Troyanovsky/Local-LLM-Comparison-Colab-UI solve, and who is the primary audience?passAI did not name Troyanovsky/Local-LLM-Comparison-Colab-UI — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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Troyanovsky/Local-LLM-Comparison-Colab-UI — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite