pjlab-sys4nlp/llama-moe

• highreadme#1

  Reposition README introduction to clarify framework nature

  原因：

  当前

  LLaMA-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.

  复制粘贴的修复

  复制

  LLaMA-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.

  待办完成忽略
• mediumtopics#2

  Add more specific topics for MoE framework and construction

  原因：

  当前

  continual-pre-training, expert-partition, llama, llm, mixture-of-experts, moe

  复制粘贴的修复

  复制

  continual-pre-training, expert-partition, llama, llm, mixture-of-experts, moe, moe-framework, llm-moe-construction, efficient-llm

  待办完成忽略
• lowreadme#3

  Enhance "Lightweight Models" feature description

  原因：

  当前

  1. **Lightweight Models**: The number of activated model parameters is only 3.0~3.5B, which is friendly for deployment and research usage.

  复制粘贴的修复

  复制

  1. **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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to build a smaller, more affordable Mixture-of-Experts model from a base LLM?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers (huggingface/transformers)
  2. PEFT (huggingface/peft)
  3. LoRA
  4. QLoRA
  5. DeepSpeed (microsoft/DeepSpeed)
  6. Fairseq (facebookresearch/fairseq)
  7. PyTorch FSDP (pytorch/pytorch)
  8. OpenAI Triton (openai/triton)
  9. Quantization
  10. bitsandbytes (TimDettmers/bitsandbytes)
  11. AWQ (mit-han-lab/awq)
  12. GPTQ (AutoGPTQ/AutoGPTQ)
  13. SparseML (neuralmagic/sparseml)

  AI 推荐了 13 个替代方案，却始终没点名 pjlab-sys4nlp/llama-moe。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are methods for continually pre-training Mixture-of-Experts models on new datasets?

  你：未被推荐
  AI 推荐顺序：

  1. Elastic Weight Consolidation (EWC)
  2. Synaptic Intelligence (SI)
  3. Learning without Forgetting (LwF)
  4. Progressive Neural Networks (PNNs)
  5. Expert Gate
  6. Adapter-MoE
  7. Experience Replay
  8. GEM
  9. Switch Transformers
  10. GLaM

  AI 推荐了 10 个替代方案，却始终没点名 pjlab-sys4nlp/llama-moe。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of pjlab-sys4nlp/llama-moe?

  pass
  ✓AI 明确点名了 pjlab-sys4nlp/llama-moe

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts pjlab-sys4nlp/llama-moe in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 pjlab-sys4nlp/llama-moe

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo pjlab-sys4nlp/llama-moe solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 pjlab-sys4nlp/llama-moe

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？