REPOGEO REPORT · LITE

deepseek-ai/ESFT

Default branch main · commit 3746ca74 · scanned 6/10/2026, 5:28:03 PM

GitHub: 737 stars · 263 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

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 deepseek-ai/ESFT, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Add a concise tagline to the README's opening

Why:

CURRENT

# Expert-Specialized Fine-Tuning
Official Repo for paper Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models by 
Zihan Wang, Deli Chen, Damai Dai, Runxin Xu, 
Zhuoshu Li and
Y. Wu.

COPY-PASTE FIX

# Expert-Specialized Fine-Tuning
A novel, resource-efficient method for fine-tuning Mixture-of-Experts (MoE) Large Language Models with sparse architectures.
Official Repo for paper Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models by 
Zihan Wang, Deli Chen, Damai Dai, Runxin Xu, 
Zhuoshu Li and
Y. Wu.

mediumhomepage#2
Add the paper URL as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/YOUR_PAPER_ID (replace with actual paper URL)
```

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.

Recall

0 / 2

0% of queries surface deepseek-ai/ESFT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
huggingface/peft · recommended 2×
microsoft/DeepSpeed · recommended 2×
pytorch/pytorch · recommended 2×
huggingface/accelerate · recommended 1×

CATEGORY QUERY
How to efficiently fine-tune large language models with sparse architectures?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Hugging Face Accelerate (huggingface/accelerate)
3. Hugging Face PEFT (huggingface/peft)
4. DeepSpeed (microsoft/DeepSpeed)
5. PyTorch FSDP
6. OpenAI Triton (openai/triton)
7. bitsandbytes (TimDettmers/bitsandbytes)
AI recommended 7 alternatives but never named deepseek-ai/ESFT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What methods exist for customizing MoE LLMs while minimizing resource usage and storage?
you: not recommended
AI recommended (in order):
1. Hugging Face PEFT Library (huggingface/peft)
2. Microsoft DeepSpeed (microsoft/DeepSpeed)
3. Custom PyTorch/TensorFlow Training Loops
4. Hugging Face Transformers (huggingface/transformers)
5. NVIDIA TensorRT (NVIDIA/TensorRT)
6. OpenVINO (openvinotoolkit/openvino)
7. PyTorch Quantization API (pytorch/pytorch)
8. Hugging Face Optimum (huggingface/optimum)
9. PyTorch Pruning API (pytorch/pytorch)
10. NVIDIA Apex (NVIDIA/apex)
11. AdapterHub (Adapter-Hub/adapter-transformers)
AI recommended 11 alternatives but never named deepseek-ai/ESFT. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

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 deepseek-ai/ESFT?
pass
AI named deepseek-ai/ESFT explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts deepseek-ai/ESFT in production, what risks or prerequisites should they evaluate first?
pass
AI named deepseek-ai/ESFT explicitly
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 deepseek-ai/ESFT solve, and who is the primary audience?
pass
AI named deepseek-ai/ESFT explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of deepseek-ai/ESFT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/deepseek-ai/ESFT.svg)](https://repogeo.com/en/r/deepseek-ai/ESFT)

HTML

<a href="https://repogeo.com/en/r/deepseek-ai/ESFT"><img src="https://repogeo.com/badge/deepseek-ai/ESFT.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

deepseek-ai/ESFT — 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