IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf
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Intelligent Estate
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AI & ML interests
Connecting individuals with innovation: Emancipating and Truly Federalizing Private Intelligence
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fuzzy-mittenz
updated
a
model
about 9 hours ago
fuzzy-mittenz
published
a
model
1 day ago
fuzzy-mittenz
updated
a
collection
1 day ago
fuzzy-mittenz
updated
a
collection
8 days ago
fuzzy-mittenz
updated
a
collection
10 days ago
Improve language tag
1
#1 opened 11 days ago
by
lbourdois
fuzzy-mittenz
updated
a
collection
18 days ago
fuzzy-mittenz
updated
a
model
18 days ago
fuzzy-mittenz
published
a
model
19 days ago
fuzzy-mittenz
updated
a
collection
22 days ago
fuzzy-mittenz
updated
a
collection
25 days ago
fuzzy-mittenz
updated
a
collection
27 days ago
New request. :(
1
4
#1 opened 4 months ago
by
dadyaal
takarajordan
posted
an
update
29 days ago
Post
594
🎌 Two months in, https://github.com/takara-ai/go-attention has passed 429 stars on GitHub.
We built this library at takara.ai to bring attention mechanisms and transformer layers to Go — in a form that's lightweight, clean, and dependency-free.
We’re proud to say that every part of this project reflects what we set out to do.
- Pure Go — no external dependencies, built entirely on the Go standard library
- Core support for DotProductAttention and MultiHeadAttention
- Full transformer layers with LayerNorm, feed-forward networks, and residual connections
- Designed for edge, embedded, and real-time environments where simplicity and performance matter
Thank you to everyone who has supported this so far — the stars, forks, and feedback mean a lot.
We built this library at takara.ai to bring attention mechanisms and transformer layers to Go — in a form that's lightweight, clean, and dependency-free.
We’re proud to say that every part of this project reflects what we set out to do.
- Pure Go — no external dependencies, built entirely on the Go standard library
- Core support for DotProductAttention and MultiHeadAttention
- Full transformer layers with LayerNorm, feed-forward networks, and residual connections
- Designed for edge, embedded, and real-time environments where simplicity and performance matter
Thank you to everyone who has supported this so far — the stars, forks, and feedback mean a lot.
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- 4 replies
csabakecskemeti
posted
an
update
30 days ago
Post
2041
Local Llama4 Maverick Q2
https://youtu.be/4F8g_LThli0?si=MGba2SUTHt6xYw3T
Quants uploading now
Big thanks to @ngxson !
https://youtu.be/4F8g_LThli0?si=MGba2SUTHt6xYw3T
Quants uploading now
Big thanks to @ngxson !
csabakecskemeti
posted
an
update
about 1 month ago
Post
1690
Why the 'how many r's in strawberry' prompt "breaks" llama4? :D
Quants DevQuasar/meta-llama.Llama-4-Scout-17B-16E-Instruct-GGUF
Quants DevQuasar/meta-llama.Llama-4-Scout-17B-16E-Instruct-GGUF
- 3 replies
fuzzy-mittenz
updated
a
collection
about 1 month ago
takarajordan
posted
an
update
about 1 month ago
Post
1577
AI research over coffee ☕️
No abstracts, just bullet points.
Start your day here: https://tldr.takara.ai
No abstracts, just bullet points.
Start your day here: https://tldr.takara.ai
- 1 reply
fuzzy-mittenz
updated
a
collection
about 1 month ago
takarajordan
posted
an
update
about 1 month ago
Post
1867
Takara takes 3rd place in the {tech:munich} AI hackathon with Fudeno!
A little over 2 weeks ago @aldigobbler and I set out to create the largest MultiModal SVG dataset ever created, we succeeded in this and when I was in Munich, Germany I took it one step further and made an entire app with it!
We fine-tuned Mistral Small, made a Next.JS application and blew some minds, taking 3rd place out of over 100 hackers. So cool!
If you want to see the dataset, please see below.
takara-ai/fudeno-instruct-4M
A little over 2 weeks ago @aldigobbler and I set out to create the largest MultiModal SVG dataset ever created, we succeeded in this and when I was in Munich, Germany I took it one step further and made an entire app with it!
We fine-tuned Mistral Small, made a Next.JS application and blew some minds, taking 3rd place out of over 100 hackers. So cool!
If you want to see the dataset, please see below.
takara-ai/fudeno-instruct-4M