coqui/XTTS-v2
Text-to-Speech
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serdarcaglar
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Recent Activity
new activity
5 days ago
serdarcaglar/roberta-base-turkish-scientific-cased:Adding `safetensors` variant of this model
new activity
11 days ago
marduk-ra/F5-TTS-Turkish:Any plan to change licensing to MIT or Apache 2.0?
new activity
24 days ago
marduk-ra/F5-TTS-Turkish:Technical questions
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serdarcaglar's activity
Adding `safetensors` variant of this model
#3 opened 6 days ago
by
SFconvertbot
Any plan to change licensing to MIT or Apache 2.0?
3
#10 opened 15 days ago
by
KadirErturk
Technical questions
2
#7 opened about 1 month ago
by
alien79
updated
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model
26 days ago
Update README.md
#95 opened 26 days ago
by
serdarcaglar
Onnx quantized version
3
#2 opened about 1 month ago
by
ysdede
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model
about 1 month ago
The Inference Code is not working properly
3
#1 opened about 2 months ago
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serdarcaglar
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about 2 months ago
liked
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2 months ago
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4 months ago
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4 months ago
Article
Building a Vision Mixture-of-Expert Model from several fine-tuned Phi-3-Vision Models
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6 months ago
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thomwolf's
post with 🔥❤️
6 months ago
Post
5086
A Little guide to building Large Language Models in 2024
This is a post-recording of a 75min lecture I gave two weeks ago on how to train a LLM from scratch in 2024. I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.
In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:
* finding, preparing and evaluating web scale data
* understanding model parallelism and efficient training
* fine-tuning/aligning models
* fast inference
There is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details.
Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway):
*
*
*
Here is the link to watch the lecture on Youtube: https://www.youtube.com/watch?v=2-SPH9hIKT8
And here is the link to the Google slides: https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p
Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.
This is a post-recording of a 75min lecture I gave two weeks ago on how to train a LLM from scratch in 2024. I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.
In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:
* finding, preparing and evaluating web scale data
* understanding model parallelism and efficient training
* fine-tuning/aligning models
* fast inference
There is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details.
Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway):
*
datatrove for all things web-scale data preparation: https://github.com/huggingface/datatrove*
nanotron for lightweight 4D parallelism LLM training: https://github.com/huggingface/nanotron*
lighteval for in-training fast parallel LLM evaluations: https://github.com/huggingface/lightevalHere is the link to watch the lecture on Youtube: https://www.youtube.com/watch?v=2-SPH9hIKT8
And here is the link to the Google slides: https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p
Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.
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