moonshotai/Kimi-K3
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Enhar Ormeci
ssalvo41
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liked a model 3 days ago
moonshotai/Kimi-K3 new activity 8 days ago
thinkingmachines/Inkling:Hermes tool calling isn't working with Inkling-NVFP4 updated a Space 2 months ago
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Hermes tool calling isn't working with Inkling-NVFP4
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#14 opened 10 days ago
by ssalvo41
updated a
Space 2 months ago
published a
Space 2 months ago
upvoted 2
articles 10 months ago
Article
Model statistics of the 50 most downloaded entities on Hugging Face
lbourdois
• • 44 Article
Visualizing How VLMs Work
not-lain
• • 56 reacted to kadirnar's post with 🔥 11 months ago
Post
3175
What can you do with the VyvoTTS library?
- You can train a model in a language it has never been trained in using the PT model. There’s no need for large datasets.
- With the PT model, you can easily replicate the voice of any character you want. Just 1k samples are enough.
- You can add emotion support with a small dataset.
Github: https://github.com/Vyvo-Labs/VyvoTTS
HuggingFace:
Vyvo
- You can train a model in a language it has never been trained in using the PT model. There’s no need for large datasets.
- With the PT model, you can easily replicate the voice of any character you want. Just 1k samples are enough.
- You can add emotion support with a small dataset.
Github: https://github.com/Vyvo-Labs/VyvoTTS
HuggingFace:
reacted to merve's post with 🚀 about 1 year ago
Post
3332
Bu post'u çevirebilirsiniz 🤗💗
- 6 replies
replied to ProCreations's post about 1 year ago
Or maybe do a backflip every time a Hugging Face space I want to use gives an error :D
Run with very low VRAM ??
1
#5 opened about 1 year ago
by ssalvo41
reacted to ProCreations's post with 👀 about 1 year ago
Post
1959
WOULD YOU RATHER
Instantly have 10 of the best GPUs in the world but you can only use/train/tune AI on it
Instantly own all of LLaMA 4 models but your GPU is 10 years old
Or do a backflip every time your AI has an error training (if you are a developer)
Instantly have 10 of the best GPUs in the world but you can only use/train/tune AI on it
Instantly own all of LLaMA 4 models but your GPU is 10 years old
Or do a backflip every time your AI has an error training (if you are a developer)
- 7 replies
reacted to albertvillanova's post with 🤗 over 1 year ago
Post
2927
smolagents v1.14.0 is out! 🚀
🔌 MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
🪨 Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. 💼
Full release 👉 https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
🔌 MCPClient: A sleek new client for connecting to remote MCP servers, making integrations more flexible and scalable.
🪨 Amazon Bedrock: Native support for Bedrock-hosted models.
SmolAgents is now more powerful, flexible, and enterprise-ready. 💼
Full release 👉 https://github.com/huggingface/smolagents/releases/tag/v1.14.0
#smolagents #LLM #AgenticAI
reacted to mikonvergence's post with 🧠 over 1 year ago
Post
1731
𝐌𝐄𝐒𝐀 🏔️ 𝐓𝐞𝐱𝐭-𝐛𝐚𝐬𝐞𝐝 𝐭𝐞𝐫𝐫𝐚𝐢𝐧 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥
MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations (co-registered colour and depth maps) of terrains based on text prompt conditioning.
Work developed by Paul Borne–Pons (@NewtNewt ) during his joint internship at
Adobe & ESA, and in collaboration with asterisk labs.
🏔️ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐏𝐚𝐠𝐞 : https://paulbornep.github.io/mesa-terrain/
📝 𝐏𝐫𝐞𝐩𝐫𝐢𝐧𝐭 : https://arxiv.org/abs/2504.07210
🤗 𝐌𝐨𝐝𝐞𝐥 𝐖𝐞𝐢𝐠𝐡𝐭𝐬 : NewtNewt/MESA
💾 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 : Major-TOM/Core-DEM
🧑🏻💻𝐂𝐨𝐝𝐞 : https://github.com/PaulBorneP/MESA
𝐇𝐅 𝐒𝐩𝐚𝐜𝐞: mikonvergence/MESA
MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations (co-registered colour and depth maps) of terrains based on text prompt conditioning.
Work developed by Paul Borne–Pons (@NewtNewt ) during his joint internship at
Adobe & ESA, and in collaboration with asterisk labs.
🏔️ 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐏𝐚𝐠𝐞 : https://paulbornep.github.io/mesa-terrain/
📝 𝐏𝐫𝐞𝐩𝐫𝐢𝐧𝐭 : https://arxiv.org/abs/2504.07210
🤗 𝐌𝐨𝐝𝐞𝐥 𝐖𝐞𝐢𝐠𝐡𝐭𝐬 : NewtNewt/MESA
💾 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 : Major-TOM/Core-DEM
🧑🏻💻𝐂𝐨𝐝𝐞 : https://github.com/PaulBorneP/MESA
𝐇𝐅 𝐒𝐩𝐚𝐜𝐞: mikonvergence/MESA
- 2 replies
reacted to etemiz's post with 👀 over 1 year ago
Post
2214
It looks like Llama 4 team gamed the LMArena benchmarks by making their Maverick model output emojis, longer responses and ultra high enthusiasm! Is that ethical or not? They could certainly do a better job by working with teams like llama.cpp, just like Qwen team did with Qwen 3 before releasing the model.
In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:
https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08
Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.
Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!
Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?
In 2024 I started playing with LLMs just before the release of Llama 3. I think Meta contributed a lot to this field and still contributing. Most LLM fine tuning tools are based on their models and also the inference tool llama.cpp has their name on it. The Llama 4 is fast and maybe not the greatest in real performance but still deserves respect. But my enthusiasm towards Llama models is probably because they rank highest on my AHA Leaderboard:
https://sheet.zoho.com/sheet/open/mz41j09cc640a29ba47729fed784a263c1d08
Looks like they did a worse job compared to Llama 3.1 this time. Llama 3.1 has been on top for a while.
Ranking high on my leaderboard is not correlated to technological progress or parameter size. In fact if LLM training is getting away from human alignment thanks to synthetic datasets or something else (?), it could be easily inversely correlated to technological progress. It seems there is a correlation regarding the location of the builders (in the West or East). Western models are ranking higher. This has become more visible as the leaderboard progressed, in the past there was less correlation. And Europeans seem to be in the middle!
Whether you like positive vibes from AI or not, maybe the times are getting closer where humans may be susceptible to being gamed by an AI? What do you think?
- 4 replies
reacted to merve's post with 🔥 over 1 year ago
Post
4545
sooo many open AI releases past week, let's summarize! 🤗
merve/april-11-releases-67fcd78be33d241c0977b9d2
multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)
LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork
Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)
*OS ones have Apache 2.0 or MIT licenses
merve/april-11-releases-67fcd78be33d241c0977b9d2
multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)
LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork
Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)
*OS ones have Apache 2.0 or MIT licenses
- 4 replies
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2
#16 opened over 1 year ago
by ssalvo41
updated a
model over 1 year ago
updated a
dataset over 1 year ago