---
license: gpl-3.0
base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
  - gguf
  - vision-language
  - smolvlm2
  - arianna-method
  - molequla
  - notorch
---

# molequla — weights

Weights that molequla organisms use on the phone. Code lives at
[github.com/ariannamethod/molequla](https://github.com/ariannamethod/molequla);
the log book there (`MOLEQULALOG2.md`) carries the measurements quoted below.

## ocelli/ — the eye

`ocelli` is the vision organ: SmolVLM2-500M with the Yent eye LoRA v2 merged
(SFT, 2026-05-29), converted to GGUF and run by a C inference path on
notorch. It gives an organism a sentence about a camera frame; that sentence
enters the colony as the fifth DNA source (`world`).

| file | quant | size | origin |
|---|---|---|---|
| `yent_eye_ours_q4_0.gguf` | Q4_0 | 232 MB | quantised on phone-1 with notorch `gguf_quantize` from the f16 merge |
| `yent_eye_ours_q6_k.gguf` | Q6_K | 418 MB | same |
| `yent_eye_ours_q8_0.gguf` | Q8_0 | 437 MB | same |
| `yent_eye_smolvlm2_lora_v2_q8_0.gguf` | Q8_0 | 437 MB | quantised on neo (llama.cpp) |
| `yent_eye_smolvlm2_lora_v2_mmproj_q8_0.gguf` | Q8_0 | 109 MB | vision tower + projector, required beside any decoder |
| `yent_eye_smolvlm2_lora_v2_mmproj_f16.gguf` | f16 | 199 MB | vision tower + projector |

`SHA256SUMS.txt` in the folder lists every file; the f16 decoder merge
(820 MB) is not uploaded here.

### Measured on phone-1 (Galaxy A56, Exynos 1580, cores 4-7, OpenBLAS)

One global 512×512 frame (`SMOLVLM_NOSPLIT=1`), 84-token prompt, from
`MOLEQULALOG2.md` (2026-09-13):

| decoder | gen tok/s | wall | peak RSS |
|---|---|---|---|
| `yent_eye_ours_q6_k` | 4.6 | 11.4 s | 951 MB |
| `yent_eye_smolvlm2_lora_v2_q8_0` | 6.4 | 13.5 s | 988 MB |
| f16 (not uploaded) | 1.4 | 31.2 s | 1602 MB |

Tiled prompts (13 frames, 878 tokens) cost 74-94 s per frame on this phone at
equal or worse content, so the eye runs on one global frame. The Q4_0 decoder
generated slower than Q8_0 on the tiled run (3.0 vs 5.0 tok/s) and dropped
objects from the description; Q6_K and Q8_0 reproduced the same sentence.

## Coming

Other VLMs as alternative eyes for the same organ are planned; they will land in
sibling folders with the same measurement table.

## ears/ — the hearing

`ears` is whisper on notorch: a C organ that loads OpenAI Whisper weights in
ggerganov's flat ggml format and transcribes 16 kHz mono wav with greedy decoding.
Its gate is parity with whisper.cpp: on phone-1 the log-mel is bit-identical, and
the transcripts of `jfk.wav` and of the same sentence re-recorded through the
phone microphone match token for token on both models below. Encoder outputs
differ by up to 4.9e-2 (mean 2.2e-4) because whisper.cpp keeps attention in f16.

| file | params | size | origin |
|---|---|---|---|
| `ggml-tiny.bin` | 39M, multilingual | 78 MB | OpenAI Whisper tiny, ggml conversion from whisper.cpp `models/download-ggml-model.sh` |
| `ggml-base.bin` | 74M, multilingual | 148 MB | OpenAI Whisper base, same |

Measured on phone-1, cores 4-7, four threads, `jfk.wav`: `ears` tiny 4.2 s wall,
131 MB peak RSS. Whisper weights are MIT-licensed by OpenAI; `SHA256SUMS.txt`
in the folder lists both files.