kai-os/Carnice-V2-27b
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sequelbox
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open source, infinite games. (they/them)
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reacted to Quazim0t0's post with ❤️🔥 3 days ago
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🧩 Verified neural units, now for memory & storage
Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos:
🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them.
🤗 Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr
🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager.
🤗 Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage
💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
🤗 Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve
Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts.
Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗
Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos:
🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them.
🤗 Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr
🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager.
🤗 Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage
💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
🤗 Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve
Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts.
Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗
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GGUFs are now available:
https://huggingface.co/mradermacher/Qwen3.6-27B-Esper4-i1-GGUF
https://huggingface.co/mradermacher/Qwen3.6-27B-Esper4-GGUF
more to come very soon!
https://huggingface.co/ValiantLabs/Qwen3.6-27B-Esper4
2
#2604 opened 22 days ago
by sequelbox
replied to their post 21 days ago
thank you! the Esper 4 mix is interesting because the datasets obviously have a lot of fundamental overlap; there are plenty of code tasks in Titanium/Mitakihara that could reasonably be in the Tachibana dataset, and some in Tachibana that could have gone the other way. by including the ~25% mix of 'old' queries in Titanium and Mitakihara it provides some balance here (many of the old Mitakihara queries in particular are chat/knowledge queries 'about' AI and AI-adjacent topics instead of specific tasks for the AI to perform) while still being highly relevant to Esper users.
have fun building :)
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7410
NEW RELEASE: Esper 4 is here for Qwen 3.6 27b, along with our new datasets!
- NEW DATASET: Titanium 4 maximizes DevOps and architecture helpfulness, powered by high-difficulty agentic-focused DevOps and architecture data generated with DeepSeek-V4-Pro!
- NEW DATASET: Mitakihara 2 brings AI coding and expertise data for AI development, research, deployment, interpretability, operation and experimentation!
- Improved coding performance: challenging agentic coding queries from Tachibana 4 allow Esper 4 to tackle harder coding tasks across a variety of languages!
GET ESPER 4: ValiantLabs/Qwen3.6-27B-Esper4
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
We've been working hard on Esper 4 - it's so exciting to finally bring it to everyone! We hope it helps you build.
We'll be expanding Esper 4 to more models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
The revolution is coming - we're here to fight for AI you can use and build on your own computer, not a giant corporation charging you for access at their discretion. We've seen what OpenAI, Anthropic, and the ultra-rich taking charge of the AI future looks like, and it's already very clear you won't like living in it. Choose a different future while you still can.
Open source must win.
More to come soon!
love, always,
allegra
- NEW DATASET: Titanium 4 maximizes DevOps and architecture helpfulness, powered by high-difficulty agentic-focused DevOps and architecture data generated with DeepSeek-V4-Pro!
- NEW DATASET: Mitakihara 2 brings AI coding and expertise data for AI development, research, deployment, interpretability, operation and experimentation!
- Improved coding performance: challenging agentic coding queries from Tachibana 4 allow Esper 4 to tackle harder coding tasks across a variety of languages!
GET ESPER 4: ValiantLabs/Qwen3.6-27B-Esper4
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
We've been working hard on Esper 4 - it's so exciting to finally bring it to everyone! We hope it helps you build.
We'll be expanding Esper 4 to more models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
The revolution is coming - we're here to fight for AI you can use and build on your own computer, not a giant corporation charging you for access at their discretion. We've seen what OpenAI, Anthropic, and the ultra-rich taking charge of the AI future looks like, and it's already very clear you won't like living in it. Choose a different future while you still can.
Open source must win.
More to come soon!
love, always,
allegra
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