point-alpaca
By pointnetwork
lora
Using Low-rank adaptation to quickly fine-tune diffusion models. (by cloneofsimo)
| point-alpaca | lora | |
|---|---|---|
| 9 | 83 | |
| 408 | 6,616 | |
| 0.0% | - | |
| 4.2 | 0.0 | |
| about 1 year ago | about 1 month ago | |
| Python | Jupyter Notebook | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
point-alpaca
Posts with mentions or reviews of point-alpaca.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-21.
- point-alpaca
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Pygmalion releases two new LLaMA based models: Pygmalion 7B and the roleplay oriented Metharme 7B. These are major improvements over the old Pygmalion models.
How does this perform compared to something like https://github.com/pointnetwork/point-alpaca?
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What AI models do you want me to test and judge with GPT-4? Taking suggestions from the community!
How does https://github.com/pointnetwork/point-alpaca compare? I was surprised how well the demo performed.
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Is this good idea to buy more rtx 3090?
I don't think it's worth it. The smaller models are powerful enough for most purposes. Did you try Point Alpaca? https://github.com/pointnetwork/point-alpaca
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What's the current "Best" LLaMA LoRA? or moreover what would be a good benchmark to test these against. (HF links incl in post)
It's not a LoRA, but this is the best I've tried: https://github.com/pointnetwork/point-alpaca It requires a GPU.
- Alpaca recreation without LORA ( released as a diff. )
- Goodbye Alpaca
- [D] Totally Open Alternatives to ChatGPT
lora
Posts with mentions or reviews of lora.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-07.
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You can now train a 70B language model at home
Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora
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How it feels right now
Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.
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LoRA tuning in julia
No, it's a deep learning thing
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What does Lora mean?
Low Rank Adaptation of Large Language Models.
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[D] An ELI5 explanation for LoRA - Low-Rank Adaptation.
Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.
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Combining LoRA, Retro, and Large Language Models for Efficient Knowledge Retrieval and Retention
Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.
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100K Context Windows
Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685
Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...
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President Biden meets with AI CEOs at the White House amid ethical criticism
Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685
- LoRA: Low-Rank Adaptation of Large Language Models
- LORA: Low-Rank Adaptation of Large Language Models
What are some alternatives?
When comparing point-alpaca and lora you can also consider the following projects:
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
stable-diffusion-webui - Stable Diffusion web UI
alpaca-lora - Instruct-tune LLaMA on consumer hardware
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
petals - 🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
sd_dreambooth_extension
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
kohya-trainer - Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning
ControlNet - Let us control diffusion models!
sd-webui-additional-networks
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
stablediffusion - High-Resolution Image Synthesis with Latent Diffusion Models
point-alpaca vs stanford_alpaca
lora vs stable-diffusion-webui
point-alpaca vs alpaca-lora
lora vs LyCORIS
point-alpaca vs petals
lora vs sd_dreambooth_extension
point-alpaca vs awesome-totally-open-chatgpt
lora vs kohya-trainer
lora vs ControlNet
lora vs sd-webui-additional-networks
lora vs diffusers
lora vs stablediffusion