brev-cli
modal-examples
| brev-cli | modal-examples | |
|---|---|---|
| 7 | 9 | |
| 197 | 572 | |
| 1.0% | 5.6% | |
| 7.9 | 9.5 | |
| 4 days ago | 5 days ago | |
| Go | Python | |
| MIT License | MIT License |
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brev-cli
- Brev: Start fine-tuning and training models in < 10 minutes
- OpenLLaMA: An Open Reproduction of LLaMA
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Using the cloud or buying a GPU
I don't have a PC right now that will run StableDiffusion. I can build one but I think I'm going to need a pretty powerful GPU which I'm not sure I can afford right now. I started using something called Brev https://brev.dev/ (no, I don't work there just found it searching). It's pretty affordable and super easy to setup.
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is there a good guide on how to train an ai to simulate your own art work?
I just finished listening to an episode of the Practical AI podcast, where they talked with Nader Khalil from brev.dev. They talked a little bit about setting up dreambooth and training it with ten images in about 4 minutes. I havent tested it, but it is worth a try. Brev.dev is a way to set up virtual machines and developement environments. Would love to heard from people who have used it.
- New AI edits images based on text instructions (instructPix2Pix/imaginAIry)
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Tensorbook
R.I.P. battery.
Personally I've been using Brev [1] to do my cloud training, you get a cloud GPU instance that you can upgrade/downgrade on the fly, and makes supports VS Code out of the box.
[1] https://brev.dev/
- Brev
modal-examples
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Show HN: Real-time image autocomplete in <100 lines of code with SDXL Lightning
We made a small app for SDXL Lightning, running your own Python code on GPUs. It generates images in real time.
https://potatoes.ai/
We know there was a fal.ai post yesterday, and that got a lot of interest, but we also made this demo yesterday and didn't share — just wanted to mention it as an alternative option for people who like running their own code and custom models instead of using a prebuilt API provider.
The backend code is open-source too and you can deploy it yourself: https://github.com/modal-labs/modal-examples/blob/main/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl_lightning.py
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Our startup has docs issues and it is costing us prospects. What things can you share to help us?
The startup I work at is relatively pretty good at documentation engineering. We have written code to test the code snippets in docstrings (https://github.com/modal-labs/pytest-markdown-docs) and we have written code to do synthetic monitoring testing of the examples in our examples repo (https://github.com/modal-labs/modal-examples). We are also diligent about putting using Python's warnings library to handle API deprecation, and treat deprecation warnings as errors internally, ensuring our own code samples and examples are most up-to-date.
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OpenLLaMA: An Open Reproduction of LLaMA
You can get it running with one Python script on Modal.com :)
https://github.com/modal-labs/modal-examples/blob/main/06_gp...
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Whispers AI Modular Future
This demo lets you choose the podcast, and is open-source: https://modal-labs--whisper-pod-transcriber-fastapi-app.moda...
https://github.com/modal-labs/modal-examples/tree/main/06_gp...
Transcribes 1hr of audio in roughly 1min, using parallelisation across CPUs.
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Show HN: PodText.ai – Search anything said on a podcast, Highlight text to play
This demo is open-source: https://github.com/modal-labs/modal-examples/tree/main/06_gp....
https://modal-labs--whisper-pod-transcriber-fastapi-app.moda...
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Show HN: Stable Diffusion Pokémon Cards
It's become so easy to stick together ML models, often without training most or all of them yourself.
*video demo:* https://youtu.be/mQsMuM8d4Qc
*cloud platform:* https://modal.com
*code*: https://github.com/modal-labs/modal-examples/tree/main/06_gp...
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How can machine learning help us learn languages better?
Transcription - OpenAI just released Whisper. Check out what it can do with podcasts
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[P] Transcribe any podcast episode in just 1 minute with optimized OpenAI/whisper
Here's the source code.
What are some alternatives?
EasyLM - Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
sd_dreambooth_extension
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
SRNet - A tensorflow reproducing of paper “Editing Text in the wild”
WAAS - Whisper as a Service (GUI and API with queuing for OpenAI Whisper)
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
stable-diffusion-webui - Stable Diffusion web UI
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
frogbase - Transform audio-visual content into navigable knowledge.