REPOGEO REPORT · LITE
pjlab-sys4nlp/llama-moe
Default branch main · commit b17aff43 · scanned 5/24/2026, 2:42:03 PM
GitHub: 1,001 stars · 60 forks
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 pjlab-sys4nlp/llama-moe, 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 introduction to clarify framework nature
Why:
CURRENTLLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA and SlimPajama. We build LLaMA-MoE with the following two steps: 1. Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts. 2. Continually pre-train the initialized MoE model with an optimized data sampling weights from Sheared LLaMA and filtered datasets from SlimPajama.
COPY-PASTE FIXLLaMA-MoE is an open-source framework and methodology for efficiently building and continually pre-training Mixture-of-Expert (MoE) models directly from LLaMA and SlimPajama. Unlike general LLM training libraries, LLaMA-MoE provides a specific two-step process: 1. Partitioning LLaMA's FFNs into sparse experts with top-K gates. 2. Continually pre-training the initialized MoE model using optimized data sampling.
- mediumtopics#2Add more specific topics for MoE framework and construction
Why:
CURRENTcontinual-pre-training, expert-partition, llama, llm, mixture-of-experts, moe
COPY-PASTE FIXcontinual-pre-training, expert-partition, llama, llm, mixture-of-experts, moe, moe-framework, llm-moe-construction, efficient-llm
- lowreadme#3Enhance "Lightweight Models" feature description
Why:
CURRENT1. **Lightweight Models**: The number of activated model parameters is only 3.0~3.5B, which is friendly for deployment and research usage.
COPY-PASTE FIX1. **Lightweight Models**: The number of activated model parameters is only 3.0~3.5B, which is friendly for deployment and research usage, making MoE models more accessible and affordable to build from base LLMs like LLaMA.
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.
- huggingface/transformers · recommended 1×
- huggingface/peft · recommended 1×
- LoRA · recommended 1×
- QLoRA · recommended 1×
- microsoft/DeepSpeed · recommended 1×
- CATEGORY QUERYHow to build a smaller, more affordable Mixture-of-Experts model from a base LLM?you: not recommendedAI recommended (in order):
- Hugging Face Transformers (huggingface/transformers)
- PEFT (huggingface/peft)
- LoRA
- QLoRA
- DeepSpeed (microsoft/DeepSpeed)
- Fairseq (facebookresearch/fairseq)
- PyTorch FSDP (pytorch/pytorch)
- OpenAI Triton (openai/triton)
- Quantization
- bitsandbytes (TimDettmers/bitsandbytes)
- AWQ (mit-han-lab/awq)
- GPTQ (AutoGPTQ/AutoGPTQ)
- SparseML (neuralmagic/sparseml)
AI recommended 13 alternatives but never named pjlab-sys4nlp/llama-moe. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are methods for continually pre-training Mixture-of-Experts models on new datasets?you: not recommendedAI recommended (in order):
- Elastic Weight Consolidation (EWC)
- Synaptic Intelligence (SI)
- Learning without Forgetting (LwF)
- Progressive Neural Networks (PNNs)
- Expert Gate
- Adapter-MoE
- Experience Replay
- GEM
- Switch Transformers
- GLaM
AI recommended 10 alternatives but never named pjlab-sys4nlp/llama-moe. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- 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 pjlab-sys4nlp/llama-moe?passAI named pjlab-sys4nlp/llama-moe explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts pjlab-sys4nlp/llama-moe in production, what risks or prerequisites should they evaluate first?passAI named pjlab-sys4nlp/llama-moe 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 pjlab-sys4nlp/llama-moe solve, and who is the primary audience?passAI named pjlab-sys4nlp/llama-moe explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
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pjlab-sys4nlp/llama-moe — 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