abacusai/Smaug-Mini
Image-Text-to-Text • 28B • Updated • 119 • 7
Manuel Barca-Grama
jackangel
AI & ML interests
None yet
Recent Activity
liked a model 2 days ago
abacusai/Smaug-Mini liked a model 3 days ago
TokenRhythm/NeoHorse-1-9B-GGUF liked a model 5 days ago
m-a-p/YuE2-3BOrganizations
None yet
liked a
model 2 days ago
liked a
model 3 days ago
liked a
model 5 days ago
reacted to OppaAI's post with 🔥 5 days ago
Post
3335
Got side-tracked this week building an app for Learning and Practicing Japanese. It's not too fancy and there will some flaws here and there. But since I'm just using it to learn and practice Japanese myself, I think I will just finalize it now and move on back to review Phase 1 of the AI Agent itself.
Did not use much of the LLM to gen the vocab.
Hallucination happens sometimes causing gibberish and mistakes in the phrases. Thanks evanclan/OpenJLPT (https://github.com/evanclan/OpenJLPT) for providing the datasets for N5->N1 vocabs and grammar datasets.
My LLM server is mainly doing conversation practice, spawning extra vocabs, and for ASR/TTS voice input/output.
🔗Front-End app: https://github.com/OppaAI/Aiko-Lingo
🔗Back-End server: https://github.com/OppaAI/Aiko-chan
Did not use much of the LLM to gen the vocab.
Hallucination happens sometimes causing gibberish and mistakes in the phrases. Thanks evanclan/OpenJLPT (https://github.com/evanclan/OpenJLPT) for providing the datasets for N5->N1 vocabs and grammar datasets.
My LLM server is mainly doing conversation practice, spawning extra vocabs, and for ASR/TTS voice input/output.
🔗Front-End app: https://github.com/OppaAI/Aiko-Lingo
🔗Back-End server: https://github.com/OppaAI/Aiko-chan
liked a
model 6 days ago
liked a
model 8 days ago
liked 2
models 9 days ago
liked a
model 11 days ago
liked 2
models 13 days ago
liked a
model 16 days ago
liked a
model 28 days ago
liked a
model about 1 month ago
liked a
dataset about 1 month ago
liked a
model about 1 month ago
liked a
model about 2 months ago
reacted to FredyRivera-dev's post with 🚀 about 2 months ago
Post
6407
We wrote a full technical guide on how to train a bilingual (ES/EN) LLM from scratch: TinyQwen.
Covers:
- Hybrid architecture based on Qwen3.5
- Pre-training with 15B tokens
- Cost benchmark between H200 and B200
- Post-training with SFT + LoRA
- Full code and data, open source
With ~$11 of compute on an H200 we ran an initial training run, enough to validate the full architecture and pipeline.
Blog post: https://aquiles-ai.vercel.app/blog/tinyqwen-from-scratch
Technical feedback welcome, especially from anyone looking to replicate the pipeline with more compute.
Covers:
- Hybrid architecture based on Qwen3.5
- Pre-training with 15B tokens
- Cost benchmark between H200 and B200
- Post-training with SFT + LoRA
- Full code and data, open source
With ~$11 of compute on an H200 we ran an initial training run, enough to validate the full architecture and pipeline.
Blog post: https://aquiles-ai.vercel.app/blog/tinyqwen-from-scratch
Technical feedback welcome, especially from anyone looking to replicate the pipeline with more compute.
- 3 replies
liked 2
models about 2 months ago
