Post
1091
Just created a Gradio space for playing with the new OAI realtime voice API!
freddyaboulton/openai-realtime-voice
freddyaboulton/openai-realtime-voice
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freddyaboulton
posted
an
update
6 days ago
freddyaboulton
posted
an
update
7 days ago
Post
448
Gemini can talk 🗣️
Check out the new multimodal API from Google on @akhaliq 's anychat or my space. It's very fast and smart 🍓
https://huggingface.co/spaces/freddyaboulton/gemini-voicehttps://huggingface.co/spaces/akhaliq/anychat
Check out the new multimodal API from Google on @akhaliq 's anychat or my space. It's very fast and smart 🍓
https://huggingface.co/spaces/freddyaboulton/gemini-voicehttps://huggingface.co/spaces/akhaliq/anychat
- 1 reply
freddyaboulton
posted
an
update
12 days ago
Post
1817
Version 0.0.21 of gradio-pdf now properly loads chinese characters!
freddyaboulton
posted
an
update
12 days ago
Post
1511
Hello Llama 3.2! 🗣️🦙
Build a Siri-like coding assistant that responds to "Hello Llama" in 100 lines of python! All with Gradio, webRTC 😎
freddyaboulton/hey-llama-code-editor
Build a Siri-like coding assistant that responds to "Hello Llama" in 100 lines of python! All with Gradio, webRTC 😎
freddyaboulton/hey-llama-code-editor
freddyaboulton
posted
an
update
14 days ago
Post
1050
Just created a cookbook of real time audio/video spaces created using Gradio and WebRTC ⚡️
Use this and the [docs](https://freddyaboulton.github.io/gradio-webrtc/) to get started building the next gen of AI apps!
freddyaboulton/gradio-webrtc-cookbook-6758ba7745aeca7b1be7de0f
Use this and the [docs](https://freddyaboulton.github.io/gradio-webrtc/) to get started building the next gen of AI apps!
freddyaboulton/gradio-webrtc-cookbook-6758ba7745aeca7b1be7de0f
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- 2 replies
Post
7191
Looks like Q4_0_N_M file types are going away
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
- 12 replies
Chat Template
2
#5 opened 19 days ago
by
isr431
juliensimon
updated
a
Space
15 days ago
chargoddard
updated
a
dataset
19 days ago
Crystalcareai
updated
a
model
20 days ago
Question about model's origin
2
#2 opened 20 days ago
by
sometimesanotion
Fix tokenizer.json with file from Qwen/Qwen2.5-14B
1
#3 opened 20 days ago
by
MariusNocturnum
use the original Qwen2.5-14B-Instruct tokenizer
1
#4 opened 20 days ago
by
MaziyarPanahi
MaziyarPanahi
updated
a
model
20 days ago
use the original Qwen2.5-14B-Instruct tokenizer
1
#4 opened 20 days ago
by
MaziyarPanahi
Adding Evaluation Results
#1 opened 20 days ago
by
leaderboard-pr-bot
Crystalcareai
updated
a
model
21 days ago
Post
10248
Old mixtral model quants may be broken!
Recently Slaren over on llama.cpp refactored the model loader - in a way that's super awesome and very powerful - but with it came breaking of support for "split tensor MoE models", which applies to older mixtral models
You may have seen my upload of one such older mixtral model, ondurbin/bagel-dpo-8x7b-v0.2, and with the newest changes it seems to be able to run without issue
If you happen to run into issues with any other old mixtral models, drop a link here and I'll try to remake them with the new changes so that we can continue enjoying them :)
Recently Slaren over on llama.cpp refactored the model loader - in a way that's super awesome and very powerful - but with it came breaking of support for "split tensor MoE models", which applies to older mixtral models
You may have seen my upload of one such older mixtral model, ondurbin/bagel-dpo-8x7b-v0.2, and with the newest changes it seems to be able to run without issue
If you happen to run into issues with any other old mixtral models, drop a link here and I'll try to remake them with the new changes so that we can continue enjoying them :)
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- 1 reply
Crystalcareai
updated
2
datasets
25 days ago