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deep-charger
baya1116/deep-charger
deep-charger is a machine learning model from baya1116. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as mit.
This repo is the complete stack of the "Deep Charger" iOS app: the shipped model weights AND the app's Swift project. Everything runs fully on-device (no server): MLX 4-bit LLM + SP hypernetwork pooler (bounded-memory…
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Updated Jul 12, 2026
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1.3 GB
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.safetensors1.6 GB · 99%
At a glance
- Library
- mlx
- License
- mit
- Access
- Public
- Created
- Jul 12, 2026
- Updated
- Jul 12, 2026
- SHA
- 7321f394
Base models
Model card
From the Hugging Face model README
Deep Charger — shipped on-device stack (iOS)
This repo is the complete stack of the "Deep Charger" iOS app: the shipped model weights AND the app's Swift project. Everything runs fully on-device (no server): MLX 4-bit LLM + SP hypernetwork pooler (bounded-memory long-context compression) + small task heads.
Contents
| path | what it is |
|---|---|
model/ | LLM: DeepSeek-R1-Distill-Qwen-1.5B, full-fine-tuned (Phase15 FFT), MLX 4-bit |
assets/pooler.safetensors | SP hypernet pooler (fp32 canonical) — compresses past tokens into 32 soft-prompt slots |
assets/pooler_q4.safetensors | 4-bit pooler actually loaded on iPhone |
assets/pooler_config.json | pooler config |
assets/intent_head.json, assets/specificity_head.json, assets/trigger_head.json | routing / WM-pinning / recall-trigger heads |
assets/phaseb_indexer.safetensors, assets/recall_gate_v2.safetensors, assets/trigger_gate_v3.safetensors | retrieval / recall gates |
app/ | the iOS app's Swift project (mlx-swift port: Pooler.swift, SPQwen2.swift input-embeddings + cache-trim, SPGenerator.swift, tiered memory + recall triggers). Team ID redacted; build with your own signing. |
Why the app source is here
The runtime/operational side (decode policy, SP eviction scheduling, recall triggering, translation UX) is as important as the weights — this repo keeps weights + operations in ONE place.
Note: mlx-swift does not run on the iOS Simulator (Metal); validate on macOS or a real iPhone GPU.
Architecture (1 line)
1.5B reasoner + recurrent attention-pooling hypernet: past context is evicted into 32 SP vectors (peak memory ~ f(batch), independent of context length) with a raw-token recency window; the app adds tiered memory + verbatim recall triggers on top.
Related repos
baya1116/hypernet-sp-distill— research workspace (training runs, experiments; messy by design)baya1116/Phase15-DeepSeek-FFT— the FFT training phase this model came from
- Library
- mlx
- License
- mit
- Created
- Jul 12, 2026
- Updated
- Jul 12, 2026