LuxTTS
🚀
4
Space for LuxTTS: a 150x realtime voice cloning TTS model
Yatharth Sharma
YatharthS
AI & ML interests
TTS, speech generation, Agents, MCP
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Your Space has a generation error
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#1 opened about 4 hours ago
by
MihaiPopa-1
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Update README.md
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#3 opened 4 days ago
by
Meyer54
replied to
their
post
5 days ago
Hey, I am working on a new TTS model called LuxVoice which will include this instead and I’ll try my best to convert this to onnx as well.
updated
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Space
7 days ago
replied to
their
post
8 days ago
Yeah seems very cool, great work!
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9 days ago
reacted to
Ujjwal-Tyagi's
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9 days ago
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2562
I am very excited to see the release of
nyuuzyou/gitee-code. This is exactly what I have been looking for. Thank you to
@nyuuzyou
for his hard work on this.
- 3 replies
reacted to
dhruv3006's
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9 days ago
Post
2685
Voiden gives you two ways to work with GraphQL - so you can focus on writing and testing queries with confidence.
1. Importing a GraphQL Schema File
You can import a GraphQL schema file such as .graphql or .gql directly into Voiden.
When you do this:
- Voiden reads all types, queries, mutations, and subscriptions from the schema
- The schema becomes available locally and works well in offline scenarios
- You get a stable, version-controlled setup that aligns nicely with Git workflows
This approach is ideal when you already have the schema file and want full control over it.
2. Using GraphQL Introspection
Alternatively, you can provide a GraphQL endpoint URL to Voiden.
In this case :
- Voiden make an introspection query to the GraphQL server
- The server returns all available types, queries, mutations, and subscriptions
- Voiden automatically loads this information so you can start querying immediately
This option is perfect for quickly exploring a live GraphQL API or when the schema file is not available locally.
Use GraphQL in our beta version : https://voiden.md/beta
1. Importing a GraphQL Schema File
You can import a GraphQL schema file such as .graphql or .gql directly into Voiden.
When you do this:
- Voiden reads all types, queries, mutations, and subscriptions from the schema
- The schema becomes available locally and works well in offline scenarios
- You get a stable, version-controlled setup that aligns nicely with Git workflows
This approach is ideal when you already have the schema file and want full control over it.
2. Using GraphQL Introspection
Alternatively, you can provide a GraphQL endpoint URL to Voiden.
In this case :
- Voiden make an introspection query to the GraphQL server
- The server returns all available types, queries, mutations, and subscriptions
- Voiden automatically loads this information so you can start querying immediately
This option is perfect for quickly exploring a live GraphQL API or when the schema file is not available locally.
Use GraphQL in our beta version : https://voiden.md/beta
reacted to
sequelbox's
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9 days ago
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NEW RELEASE: it's here! Meet the newest member of the Valiant crew: Guardpoint, our new medical reasoning model!
- Trained on medical knowledge, management, diagnosis, and tasks from DeepSeek-V3.2-Speciale!
- 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 it now:
Guardpoint for Qwen 3 32B: ValiantLabs/Qwen3-32B-Guardpoint
Guardpoint for Qwen 3 14B: ValiantLabs/Qwen3-14B-Guardpoint
Powered by our new structured medical reasoning dataset: sequelbox/Superpotion-DeepSeek-V3.2-Speciale
We've been working hard on Guardpoint; we're really excited to share it with everyone!
We'll be bringing Guardpoint to more models soon, along with further releases for the Shining Valiant and Esper series!
Get our experimental models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
Get our reasoning datasets: https://huggingface.co/collections/sequelbox/reasoning-datasets
Help support our releases, donations used for our experimental models and datasets: sequelbox/SupportOpenSource
2026 is going to be an amazing year for open source AI! It's time for the AI revolution you need; from the bottom up, built together by all of us.
for love, friendship, and better days,
allegra
- Trained on medical knowledge, management, diagnosis, and tasks from DeepSeek-V3.2-Speciale!
- 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 it now:
Guardpoint for Qwen 3 32B: ValiantLabs/Qwen3-32B-Guardpoint
Guardpoint for Qwen 3 14B: ValiantLabs/Qwen3-14B-Guardpoint
Powered by our new structured medical reasoning dataset: sequelbox/Superpotion-DeepSeek-V3.2-Speciale
We've been working hard on Guardpoint; we're really excited to share it with everyone!
We'll be bringing Guardpoint to more models soon, along with further releases for the Shining Valiant and Esper series!
Get our experimental models: https://huggingface.co/collections/sequelbox/experimental-reasoning-models
Get our reasoning datasets: https://huggingface.co/collections/sequelbox/reasoning-datasets
Help support our releases, donations used for our experimental models and datasets: sequelbox/SupportOpenSource
2026 is going to be an amazing year for open source AI! It's time for the AI revolution you need; from the bottom up, built together by all of us.
for love, friendship, and better days,
allegra
reacted to
MikeDoes's
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9 days ago
Post
216
The future of AI privacy isn't just in the cloud; it's on your device. But how do we build and validate these tools?
A new paper on "Rescriber" explores this with a tool that uses smaller LLMs for on-device anonymization. Building and validating such tools requires a strong data foundation. We're excited to see that the researchers used the Ai4Privacy open dataset to create their performance benchmarks.
This is our mission in action: providing the open-source data that helps innovators build and test better solutions that will give users more control over their privacy. It's a win for the community when our data helps prove the feasibility of on-device AI for data minimization, with reported user perceptions on par with state-of-the-art cloud models.
Shoutout to Jijie Zhou, Eryue Xu, Yaoyao Wu, and Tianshi Li on this one!
🔗 Check out the research to see how on-device AI, powered by solid data, is changing the game: https://dl.acm.org/doi/pdf/10.1145/3706598.3713701
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
A new paper on "Rescriber" explores this with a tool that uses smaller LLMs for on-device anonymization. Building and validating such tools requires a strong data foundation. We're excited to see that the researchers used the Ai4Privacy open dataset to create their performance benchmarks.
This is our mission in action: providing the open-source data that helps innovators build and test better solutions that will give users more control over their privacy. It's a win for the community when our data helps prove the feasibility of on-device AI for data minimization, with reported user perceptions on par with state-of-the-art cloud models.
Shoutout to Jijie Zhou, Eryue Xu, Yaoyao Wu, and Tianshi Li on this one!
🔗 Check out the research to see how on-device AI, powered by solid data, is changing the game: https://dl.acm.org/doi/pdf/10.1145/3706598.3713701
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
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9 days ago
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GLM-Image from Z.ai is out 🔥
It was fully trained on Ascend Atlas 800T A2 with MindSpore, probably the first SOTA multimodal model fully trained on domestic chips 👀
zai-org/GLM-Image
✨ Hybrid Architecture: combined autoregressive + diffusion design delivers strong semantic alignment with high-fidelity details
✨ Strong performance in long, dense, and multilingual text rendering
✨ MIT licensed (VQ tokenizer & ViT weights under Apache 2.0)
✨ Now live on Hugging Face inference provider 🤗
It was fully trained on Ascend Atlas 800T A2 with MindSpore, probably the first SOTA multimodal model fully trained on domestic chips 👀
zai-org/GLM-Image
✨ Hybrid Architecture: combined autoregressive + diffusion design delivers strong semantic alignment with high-fidelity details
✨ Strong performance in long, dense, and multilingual text rendering
✨ MIT licensed (VQ tokenizer & ViT weights under Apache 2.0)
✨ Now live on Hugging Face inference provider 🤗