also, qwen 3.8 releases will be coming soon! literally no money right now ๐ as soon as we can!
t.d.a.g. PRO
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GGUFs:
https://huggingface.co/bartowski/ValiantLabs_Muse-Glimmer-30B-Esper4-GGUF
https://huggingface.co/mradermacher/Muse-Glimmer-30B-Esper4-GGUF
https://huggingface.co/mradermacher/Muse-Glimmer-30B-Esper4-i1-GGUF
https://huggingface.co/mradermacher/Muse-Glimmer-30B-Esper4-i1-GGUF
https://huggingface.co/mradermacher/Muse-Glimmer-30B-Tachibana-Agent-GGUF
this is a great suggestion for evaluation, thank you!
in general, detailed evaluation is an allocation question for us - with very limited funds currently available for compute, our general priority is to put all of them into datasets and models, to maximize the impact we can make on the open source ecosystem surviving and eventually winning. that is the whole point of our work, to achieve this result :) we can certainly provide more context by offering more benchmarks etc, but ultimately the user will always be better positioned to evaluate their own use cases with their own frameworks and areas of expertise. will still do it if we can!
- Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps!
- Tachibana-Agent, trained only on code for dedicated, predictable deployment!
GET OUR NEW MODELS:
ValiantLabs/Muse-Glimmer-30B-Esper4
sequelbox/Muse-Glimmer-30B-Tachibana-Agent
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
Also, starting work on the SV4 datasets :)
More to come soon!
love,
allegra
added some Esper 4 merges for Qwen 3.6 and Gemma 4:
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4Carnice
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4Grug
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4Musa
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4Tachibana
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4Tess
https://huggingface.co/sequelbox/Qwen3.6-27B-Esper4ThinkingCap
https://huggingface.co/sequelbox/gemma-4-12B-it-Esper4FableComposer
https://huggingface.co/sequelbox/gemma-4-12B-it-Esper4Grug
https://huggingface.co/sequelbox/gemma-4-12B-it-Esper4Tachibana
Esper 4 is now available for Gemma 4 12B: https://huggingface.co/ValiantLabs/gemma-4-12B-it-Esper4
- Esper 4, our flagship agentic coder: specialist in coding, architecture, DevOps, and MLOps!
- Tachibana-Agent, trained only on code for dedicated, predictable deployment!
- Guardpoint, our structured medical reasoning model: medical diagnosis, management, knowledge, and understanding in structured, concise form!
GET OUR NEW MODELS:
ValiantLabs/gemma-4-12B-it-Esper4
sequelbox/gemma-4-12B-it-Tachibana-Agent
ValiantLabs/gemma-4-12B-it-Guardpoint
Get the datasets for your own training:
sequelbox/Titanium4-DeepSeek-V4-Pro
sequelbox/Mitakihara2-DeepSeek-V4-Pro
sequelbox/Tachibana4-DeepSeek-V4-Pro
sequelbox/Superpotion-DeepSeek-V3.2-Speciale
Esper 4 is also available for Qwen 3.6 27B: ValiantLabs/Qwen3.6-27B-Esper4
We'll be expanding Esper 4 to more models and releasing new models as funding allows - donate for more, faster, better models and datasets: sequelbox/SupportOpenSource
More to come soon!
go build stuff :)
allegra
Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain โ N/N), I applied the same discipline to memory and storage. Three new repos:
๐ท neural-ddr โ 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 โ flip a bit in every stored byte, ECC corrects all of them.
๐ค Quazim0t0/neural-ddr ยท ๐ป https://github.com/quzi93/neural-ddr
๐๏ธ neural-storage โ a self-healing vault on a neural-verified GF(2โธ) 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 โ self-healing .pt imager.
๐ค Quazim0t0/neural-storage ยท ๐ป https://github.com/quzi93/neural-storage
๐ฟ neural-cd-preserve โ 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.
๐ค Quazim0t0/neural-cd-preserve ยท ๐ป https://github.com/quzi93/neural-cd-preserve
Build your own: golden finite function โ enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) โ train a small MLP โ verify must be bit-exact on 100% of inputs or it's rejected โ compose. Every repo ships the training + exhaustive-verification scripts.
Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults โ none of it pretends to beat entropy. Runs on modest/older hardware. ๐ค
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!
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 :)
- 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
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! ๐
Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth
Repo: https://github.com/unmodeled-tyler/vessel-browser
I wanted to share a cool feature from my open source AI native web browser, Vessel: Persistent highlights!
You can highlight anything on the page and the context is provided to the agent. It's kind of a fun way to learn about new stuff, synthesize info, or just deepen your comprehension/understanding.
Since highlights are persistent, you can close the page, come back later - and your highlights will be exactly where you left them. I've found this particularly useful when reviewing technical blogs, model cards, etc.
Check it out!
- Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topics. Synthetic prompts utilize a variety of personas, experience levels, and styles of communication to maximize real-world flexibility and usability.
- Areas of focus include back-end and front-end development, systems programming, distributed systems, performance optimization, data structures, databases and data engineering, game and mobile development, security engineering, compiler design, custom tooling, task automation, practical bugfixes, and more!
- A wide variety of emphasized languages improves development capability: Python, C, C++, C#, Go, TypeScript, Java, JavaScript, Rust, Haskell, SQL, Shell, R, Ruby, assembly code, and more!
The new dataset: sequelbox/Tachibana4-DeepSeek-V4-Pro
The new model: sequelbox/Qwen3.6-27B-Tachibana-Agent
We're thrilled to bring this to everyone - try it out and see what you think!
Tachibana 4 is the first of several datasets used for the upcoming Esper 4! See what we're working on and help our releases come out faster: sequelbox/SupportOpenSource
Open source will win :)
love,
allegra
Tachibana 4 is our upcoming agentic coding dataset:
- Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topics.
- Areas of focus include back-end and front-end development, systems programming, distributed systems, performance optimization, data structures, databases and data engineering, game and mobile development, security engineering, compiler design, custom tooling, task automation, practical bugfixes, and more!
- A wide variety of emphasized languages improves development capability: Python, C, C++, C#, Go, TypeScript, Java, JavaScript, Rust, Haskell, SQL, Shell, R, Ruby, assembly code, and more!
- Synthethic prompts utilize a variety of personas, experience levels, and styles of communication to maximize real-world flexibility and usability.
Get it now: sequelbox/Tachibana4-DeepSeek-V4-Pro-PREVIEW
These agentic datasets will power the upcoming Esper 4, and whatever you can build! We'll have more finetunes on the way as well! :) we're going to make open source better and better for your work!
If you would like to see Esper 4 and these datasets faster, this is the best way you can help us: sequelbox/SupportOpenSource
for freedom, with love,
allegra
Qwen 3.6 27B available as well: https://huggingface.co/ValiantLabs/Qwen3.6-27B-Esper3.1
Quants for both are up too:
https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Esper3.1-i1-GGUF
https://huggingface.co/mradermacher/Qwen3.6-35B-A3B-Esper3.1-GGUF
https://huggingface.co/mradermacher/Qwen3.6-27B-Esper3.1-i1-GGUF
https://huggingface.co/mradermacher/Qwen3.6-27B-Esper3.1-GGUF
- Your dedicated DevOps expert: Esper 3.1 maximizes DevOps and architecture helpfulness, powered by high-difficulty DevOps and architecture data generated with DeepSeek-V3.1-Terminus!
- Improved coding performance: challenging code-reasoning datasets stretch DeepSeek-V3.1-Terminus and DeepSeek-V3.2 to the limits, allowing Esper 3.1 to tackle harder coding tasks!
- AI to build AI: our high-difficulty AI expertise data boosts Esper 3.1's MLOps, AI architecture, AI research, and general reasoning skills.
Get it now: ValiantLabs/Qwen3.6-35B-A3B-Esper3.1
We're working on more finetunes for the newest Qwen and Gemma models, and we've also started working on the agentic-first datasets for Esper 4 :) we're going to make open source better and better for your work!
Please note that real life financial and family concerns have popped up and have imposed unfortunate limitations on our ability to devote time to our open-source work :( If you would like to see Esper 4 and our other releases speed up instead of slowing down, this is the best way you can help us: sequelbox/SupportOpenSource
No matter what, we'll keep fighting and we won't give up!
with love,
allegra
For Gemma 4 31B: Guardpoint, our medical reasoning model, trained on medical knowledge, management, diagnosis, and tasks:
- Structured medical reasoning responses are efficient and informative, cutting token costs for faster inference!
- Wide-ranging knowledge base: trained on a wide variety of medical disciplines, patient types, and query structures!
- High quality medical responses emphasize performance, brevity, specificity, statistical rationality, and openness.
Get Guardpoint for Gemma 4: ValiantLabs/gemma-4-31B-it-Guardpoint
For Gemma 4 E4B and E2B: Shining Valiant 3, our science-reasoning model!
- Science-reasoning: physics, biology, chemistry, compsci, astronomy, Earth science, and information theory.
- AI to build AI: high-quality reasoning performance on AI, MLOps, math and CUDA, complex adaptive and agentic systems, cognition, logic, linguistics, simulation, knowledge management, and more!
- Supplemented creative reasoning and general chat performance.
Get the new SV3 models:
E4B: ValiantLabs/gemma-4-E4B-it-ShiningValiant3
E2B: ValiantLabs/gemma-4-E2B-it-ShiningValiant3
We're working on several things - most excitingly, we've officially started the dataset curation process for Esper 4! We're focused on enhanced agentic capability and higher-dififculty, higher-value tasks this time, very excited to bring this to everyone when we can :)
Help support our releases, donations used for our experimental models and datasets: sequelbox/SupportOpenSource
Fight for open source with us!
for love and friendship,
allegra