google / google/gemma.cpp

Showcase / question: a board-proven offline language runtime on ESP32-C3, and whether this points to a more extreme form of language/runtime co-design

Abierto
#875 4 comentarios 0 reacciones 0 asignados Ver en GitHub
Lenguaje dominante
C++
Estrellas
7k
Forks
660
Merge medio
20 h 43 min
PR fusionados (30 d)
33

Descripción

Hi gemma.cpp folks,

I wanted to share a small but unusual language-runtime project that may be relevant to the kind of language/runtime co-design boundary this repo is already exploring.

We built a public demo line called Engram and deployed it on a commodity ESP32-C3.

Current public numbers:

* Host-side benchmark capability
* `LogiQA = 0.392523`
* `IFEval = 0.780037`

* Published board proof
* `LogiQA 642 = 249 / 642 = 0.3878504672897196`
* `host_full_match = 642 / 642`
* runtime artifact size = `1,380,771 bytes`

Important scope note:

This is **not** presented as unrestricted open-input native LLM generation on MCU.

The board-side path is closer to a flash-resident, table-driven runtime with:

* packed token weights
* hashed lookup structures
* fixed compiled probe batches
* streaming fold / checksum style execution over precompiled structures

So this is not a standard lightweight dense inference runtime on a small device. It is closer to a task-specialized language runtime whose behavior has been crystallized into a compact executable form under very severe physical constraints.

Repo:
https://github.com/Alpha-Guardian/Engram

Why I’m posting here is that gemma.cpp seems to sit at an interesting point between research-friendly implementation, low-level execution, and simplified language inference systems.

What I’d be curious about is whether systems like this should be thought of as:

* outside the normal lightweight inference-runtime family
* an extreme endpoint of language/runtime co-design
* or an early sign that some language-task capability may eventually be deployed in more specialized executable forms than even a minimalist dense runtime

If this direction is relevant to your team, I’d be glad to compare notes.

Guía de contribución

Abrir la guía de contribución

Línea de trabajo

No se menciona ningún archivo, prueba ni punto de entrada de gemma.cpp. Empieza leyendo el issue junto con el repositorio de Engram enlazado y la descripción del proyecto gemma.cpp; esto solo estaría completo después de que los maintainers decidan si la comparación de tiempos de ejecución propuesta es relevante y qué seguimiento concreto, si corresponde, pertenece a este repositorio.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
cpp
Área
embedded-iot, machine-learning
Tipo de issue
Nueva funcionalidad
Dificultad
5/5
Tiempo estimado
Más de una semana
Estado de actividad
Tranquilo
Claridad
Necesita aclaración
Aptitud para principiantes
15/100

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.