AutoArk/TinyEngram

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1191 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TinyEngram is an open research project exploring DeepSeek-AI's Engram architecture and memory injection as an alternative to LoRA for parameter-efficient fine-tuning of LLMs like Qwen and diffusion models like Stable Diffusion. It provides code, experiment logs, and technical reports showing Engram-based memory injection outperforms LoRA in parameter efficiency and resistance to catastrophic forgetting.

Use cases

• inject new concepts into Qwen LLMs without full fine-tuning
• replace LoRA with Engram-based memory injection for PEFT
• add visual concepts to Stable Diffusion models lightweightly
• reduce catastrophic forgetting during fine-tuning
• reproduce Engram architecture experiments
• research parameter-efficient adaptation of transformers

When to choose

• you want to experiment with Engram-style memory injection instead of LoRA
• you need composable, lightweight concept injection into LLMs or Stable Diffusion
• you are researching catastrophic forgetting and parameter-efficient fine-tuning

When to avoid

• you need a production-ready, stable fine-tuning framework with support guarantees
• you require a permissively licensed dependency and cannot accept unclear licensing
• you just need standard LoRA/PEFT tooling without experimental architecture changes

Facets

library · maturity experimental

machine-learning llm-training llm-inference image-processing sdk large-language-models machine-learning deep-learning image-processing artificial-intelligence python cross-platform deepseek-engram memory-injection lora peft fine-tuning qwen stable-diffusion research transformer catastrophic-forgetting gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="AutoArk/TinyEngram")