YatharthS/LuxTTS
Text-to-Speech
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Updated
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Yatharth Sharma
YatharthS
AI & ML interests
TTS, speech generation, Agents, MCP
Recent Activity
reacted
to
AbstractPhil's
post
with 🔥
2 days ago
Meet FluxLailah; https://huggingface.co/AbstractPhil/tiny-flux-deep; 220m Flux variant currently pretraining at BF16. She is experimental, does not produce solid images yet - and yet she is producing. There is both an EMA and a raw weights pair producing different images. The EMA is particularly interesting at times.
Lailah uses flan-t5-base, clip-vit-l-14, and BlackForestLabs Flux1s VAE.
SEQ limit 128, images 512x512 for now. Lailah's early form is based on three variants. TinyFlux's weights were carefully planted into a deeper structure and trained yet again - dubbed TinyFlux-Deep. This variant has 15 dual-stream blocks and 25 single-stream blocks, nearly identical weight code as Flux with a similar attention mechanism - but intentionally deviant and compacted with careful consideration to scaling and purpose of mechanisms.
She went through quite a few growing pains with her earlier attention mechanism which required a reimagining today and careful consideration of the consequences, and now I present to you the preliminary look into Lailah.
The preliminary training is still heavily under way, the mechanisms are still being augmented, and her stability is currently being measured. The potential for fidelity, depth, and quality are still in measure - so I will be shifting attention and pivoting utility based on the needs over time.
reacted
to
raincandy-u's
post
with 🔥
2 days ago
🤗 Just released Rain-100M, an experimental ~97M-parameter Qwen3-style language model trained from random initialization.
Repo: https://huggingface.co/raincandy-u/Rain-100M
Data: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu, ~3B tokens, English only
Tokenizer: custom 16k BPE, context length 4096
Architecture: 12 Transformer layers, hidden size 768, 12 heads, MLP 2048, SiLU, bf16
Rain-100M is a raw base model (not instruction-tuned or safety-aligned), aimed at small-scale research, debugging training pipelines, and CPU/edge experiments. If you run evaluations, finetunes, or visualizations with it, I would be very interested in your results!
new activity
2 days ago
YatharthS/LuxTTS:About the architecture
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