RRepoGEO

REPOGEO REPORT · LITE

SmartFlowAI/EmoLLM

Default branch main · commit 955155bb · scanned 6/20/2026, 11:12:06 AM

GitHub: 1,750 stars · 222 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 SmartFlowAI/EmoLLM, 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.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement to emphasize 'framework' and 'toolkit'

    Why:

    CURRENT
    **EmoLLM** 是一系列能够支持 **理解用户-支持用户-帮助用户** 心理健康辅导链路的心理健康大模型，由 `LLM`指令微调而来，欢迎大家star~⭐⭐。
    COPY-PASTE FIX
    **EmoLLM** 是一个全面的开源框架和工具包，专为构建、微调和部署心理健康领域的大语言模型（LLMs）而设计。它支持从数据准备、预训练、指令微调到评估和部署的完整心理健康辅导LLM开发链路，欢迎大家star~⭐⭐。
  • mediumtopics#2
    Add more specific topics and correct typo

    Why:

    CURRENT
    dataset, depoly, evaluation, llm, post-training, the-big-model-of-mental-health
    COPY-PASTE FIX
    dataset, deploy, evaluation, llm, post-training, the-big-model-of-mental-health, mental-health-ai, llm-framework, llm-toolkit, fine-tuning, llm-deployment
  • lowabout#3
    Refine the repository description to include 'framework' and 'toolkit'

    Why:

    CURRENT
    心理健康大模型 (LLM x Mental Health), Pre & Post-training & Dataset & Evaluation & Depoly & RAG, with InternLM / Qwen / Baichuan / DeepSeek / Mixtral / LLama / GLM series models
    COPY-PASTE FIX
    一个全面的心理健康大模型（LLM x Mental Health）开源框架和工具包，涵盖预训练、指令微调、数据集、评估、部署和RAG，支持InternLM / Qwen / Baichuan / DeepSeek / Mixtral / LLama / GLM系列模型。

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 SmartFlowAI/EmoLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prodigy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Prodigy · recommended 2×
  2. Hugging Face Inference Endpoints · recommended 2×
  3. Azure Machine Learning · recommended 2×
  4. Hugging Face Transformers · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What tools are available for building large language models focused on mental health support?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. DeepSpeed
    5. FSDP (Fully Sharded Data Parallel)
    6. Prodigy
    7. Label Studio
    8. Doccano
    9. Hugging Face Inference Endpoints
    10. TGI (Text Generation Inference)
    11. NVIDIA Triton Inference Server
    12. Kubernetes
    13. Docker
    14. IBM AI Fairness 360 (AIF360)
    15. Google's What-If Tool (WIT)
    16. Microsoft Fairlearn
    17. AWS (Amazon Web Services)
    18. EC2
    19. S3
    20. SageMaker
    21. Google Cloud Platform (GCP)
    22. Compute Engine
    23. Cloud Storage
    24. Vertex AI
    25. BigQuery
    26. Microsoft Azure
    27. Azure Virtual Machines
    28. Azure Blob Storage
    29. Azure Machine Learning
    30. Cognitive Services

    AI recommended 30 alternatives but never named SmartFlowAI/EmoLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I fine-tune and deploy an LLM for psychological counseling, including data and evaluation?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Mistral Large
    4. Llama 3 70B
    5. CounselChat Dataset
    6. Argilla (argilla-io/argilla)
    7. Prodigy
    8. Label Studio (heartexlabs/label-studio)
    9. Llama 3 8B
    10. Mistral 7B
    11. Mixtral 8x7B
    12. Gemma 7B
    13. Gemma 2B
    14. Falcon 40B
    15. Falcon 7B
    16. Hugging Face Transformers (huggingface/transformers)
    17. PEFT Library (huggingface/peft)
    18. Axolotl (OpenAccess-AI-Collective/axolotl)
    19. DeepSpeed (microsoft/DeepSpeed)
    20. ROUGE
    21. BLEU
    22. BERTScore (Tiiiger/bert_score)
    23. MoverScore (AIPHES/MoverScore)
    24. Amazon Mechanical Turk
    25. Appen
    26. Scale AI
    27. Hugging Face Evaluate Library (huggingface/evaluate)
    28. Google's Perspective API
    29. AWS SageMaker
    30. Google Cloud Vertex AI
    31. Azure Machine Learning
    32. Hugging Face Inference Endpoints
    33. NVIDIA Triton Inference Server (triton-inference-server/server)
    34. vLLM (vllm-project/vllm)
    35. Ollama (ollama/ollama)
    36. FastAPI (tiangolo/fastapi)
    37. Flask (pallets/flask)
    38. PyTorch (pytorch/pytorch)
    39. bitsandbytes (TimDettmers/bitsandbytes)
    40. ONNX Runtime (microsoft/onnxruntime)
    41. TensorRT

    AI recommended 41 alternatives but never named SmartFlowAI/EmoLLM. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 SmartFlowAI/EmoLLM?
    pass
    AI named SmartFlowAI/EmoLLM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts SmartFlowAI/EmoLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SmartFlowAI/EmoLLM 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 SmartFlowAI/EmoLLM solve, and who is the primary audience?
    pass
    AI named SmartFlowAI/EmoLLM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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SmartFlowAI/EmoLLM — RepoGEO report