dots-studio/dots3-note-prev
Image-Text-to-Text • 288B • Updated • 11 • 99
Djuunaa
djuna
AI & ML interests
None yet
Recent Activity
liked a model about 10 hours ago
dots-studio/dots3-note-prev liked a model 1 day ago
peculiar-ragdoll/Qwen-Sharp-Chat-TemplatesOrganizations
liked a
model about 10 hours ago
liked a
model 1 day ago
Or it could be just profit margin. Deepseek doesn't have coding agent, deepseek web chat is also outdated in term of feature.
reacted to onekq's post with 👀 7 days ago
Post
2421
DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away.
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
- 5 replies
liked a
model 11 days ago
liked a
model 22 days ago
liked a
model 25 days ago
liked a
model 26 days ago
liked 3
models about 1 month ago
reacted to stas's post with 🔥 about 1 month ago
Post
3638
After many months of intense work the
Snowflake AI Research team is happy to present to you the new open source project: Arctic RL
https://snowflake.com/en/blog/engineering/arctic-rl-open-source-backend/
- Arctic RL integrates with VeRL and SkyRL today; enable ZoRRo with one config flag, no code changes required
- ZoRRo delivers up to 6x actor-update acceleration and a 3.5x end-to-end training speedup, reducing Arctic-Text2SQL-R2 training from ~5 days to ~36 hours on 32 H200 GPUs
- Arctic-Text2SQL-R2 achieved higher accuracy scores (48.7) than Gemini 3.1 Pro (47.9) and Claude 4.7 (47.3) on Snowflake's evaluated enterprise SQL benchmark under the tested conditions
- Two open source recipes ship with this release: a text-to-SQL recipe that improved BIRD dev accuracy from 59.92% to 70.35%, and a multi-hop QA recipe that improved average accuracy from 69.6% to 72.3%
Snowflake AI Research team is happy to present to you the new open source project: Arctic RL
https://snowflake.com/en/blog/engineering/arctic-rl-open-source-backend/
- Arctic RL integrates with VeRL and SkyRL today; enable ZoRRo with one config flag, no code changes required
- ZoRRo delivers up to 6x actor-update acceleration and a 3.5x end-to-end training speedup, reducing Arctic-Text2SQL-R2 training from ~5 days to ~36 hours on 32 H200 GPUs
- Arctic-Text2SQL-R2 achieved higher accuracy scores (48.7) than Gemini 3.1 Pro (47.9) and Claude 4.7 (47.3) on Snowflake's evaluated enterprise SQL benchmark under the tested conditions
- Two open source recipes ship with this release: a text-to-SQL recipe that improved BIRD dev accuracy from 59.92% to 70.35%, and a multi-hop QA recipe that improved average accuracy from 69.6% to 72.3%
- 4 replies
Whats layerdose?
1
#1 opened about 1 month ago
by djuna
liked 6
models about 2 months ago
arbazsiddiqui/Ozan-v1-12B
Text Generation • 12B • Updated • 249 • • 10
hustvl/Moebius
Updated • 59
Casual-Autopsy/Giftige-Blume-31B-v1-StyleSwap
Text Generation • 33B • Updated • 42 • 1
Casual-Autopsy/QwEmotion-RP-Embed-0.6B-v0.1
Sentence Similarity • 0.6B • Updated • 151 • 4
dnotitia/DNA3.0-9B
Image-Text-to-Text • 9B • Updated • 330 • 2
owensong/Inflect-Nano-v1
Text-to-Speech • Updated • 226
reacted to owensong's post with 🔥 about 2 months ago
Post
7528
I just released Inflect-Nano-v1, an ultra-small 4.63 parameter text-to-speech model.
The main idea is simple: instead of only making the acoustic model tiny and relying on a larger external vocoder, Inflect-Nano-v1 keeps the complete text-to-waveform stack under 5M parameters.
Quick facts:
- 4.63M total inference parameters
- 3.46M acoustic model
- 1.17M vocoder
- 24 kHz audio
- English-only
- Single male voice
- Runs locally with a simple PyTorch inference script
Why I made it:
Most modern TTS models are much larger, and even many “small TTS” projects depend on a separate vocoder. I wanted to see how far a complete tiny TTS stack could be pushed while still producing usable speech.
It is not SOTA, and I am not trying to claim it competes with large TTS systems. The interesting part is the size-to-functionality ratio.
What works:
It can generate arbitrary English speech locally, and the model is small enough to be interesting for:
- local voice assistants
- embedded/edge experiments
- browser or WASM-style TTS exploration
- efficient inference research
- tiny-model baselines
Limitations:
The quality is still limited. It can sound robotic, stumble on difficult unseen text, and the vocoder is still a clear bottleneck. Long or unusual prompts are less reliable.
So I would frame this as a research/demo release, not a production TTS engine.
I’d love feedback from people interested in:
- tiny speech models
- vocoders
- local TTS
- efficient inference
- embedded speech synthesis
- improving small-model generalization
If people find it useful, I’m interested in putting more training budget into a stronger v2.
Model page:
owensong/Inflect-Nano-v1
The main idea is simple: instead of only making the acoustic model tiny and relying on a larger external vocoder, Inflect-Nano-v1 keeps the complete text-to-waveform stack under 5M parameters.
Quick facts:
- 4.63M total inference parameters
- 3.46M acoustic model
- 1.17M vocoder
- 24 kHz audio
- English-only
- Single male voice
- Runs locally with a simple PyTorch inference script
Why I made it:
Most modern TTS models are much larger, and even many “small TTS” projects depend on a separate vocoder. I wanted to see how far a complete tiny TTS stack could be pushed while still producing usable speech.
It is not SOTA, and I am not trying to claim it competes with large TTS systems. The interesting part is the size-to-functionality ratio.
What works:
It can generate arbitrary English speech locally, and the model is small enough to be interesting for:
- local voice assistants
- embedded/edge experiments
- browser or WASM-style TTS exploration
- efficient inference research
- tiny-model baselines
Limitations:
The quality is still limited. It can sound robotic, stumble on difficult unseen text, and the vocoder is still a clear bottleneck. Long or unusual prompts are less reliable.
So I would frame this as a research/demo release, not a production TTS engine.
I’d love feedback from people interested in:
- tiny speech models
- vocoders
- local TTS
- efficient inference
- embedded speech synthesis
- improving small-model generalization
If people find it useful, I’m interested in putting more training budget into a stronger v2.
Model page:
owensong/Inflect-Nano-v1