KoboldAI-Client
llama
Our great sponsors
KoboldAI-Client | llama | |
---|---|---|
185 | 184 | |
3,344 | 53,053 | |
- | 5.5% | |
6.3 | 8.1 | |
about 2 months ago | 19 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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.
KoboldAI-Client
- No idea what I'm doing help
-
ChatGPT users drop for the first time as people turn to uncensored chatbots
You can use KoboldAI to run a LLM locally. There are hundreds / thousands of models on hugging face. Some uncensored ones are Pygmalion AI (chatbot), Erebus (story writing AI), or Vicuna (general purpose).
-
Tips for using Kobold with Venus? I am pretty new at everything.
GPT-J 6B is a pretty weak and outdated model. Nerys 13B would probably give you better replies but they lean more towards SFW stuff. Erebus was their best model for erotic roleplay but they removed it as it went against Google's TOS. You can check out their documentation here.
-
I can't do this y'all
If you do have that kind of hardware, the next step would be looking for what model to run. I came across Kobold's models. Their main github page is here: https://github.com/KoboldAI/KoboldAI-Client
-
Question regarding model compatibility for Alpaca Turbo
Then there are graphical user interfaces like text-generation-webui and gpt4all for general purpose chat. There are also KoboldAI and SillyTavern, they have focus more on storytelling and roleplay and have tools to improve that.
-
Running Multiple AI Models Sequentially for a Conversation on a Single GPU
And finally the folks from the KoboldAi do some interesting stuff with Pseudocode and Soft-Prompts that might also be relevant.
- Summoning Life-Size Characters to Your Room: New Update for my Mixed Reality App!
- Feels like the censorship has gotten tighter recently, just me?
-
How to get a KoboldAI URL API key!
Click this link. ---> https://github.com/KoboldAI/KoboldAI-Client/tree/main
-
Difficulties installing Pygmalion 13b
Do you believe the problem could be that my KoboldAI is outdated? I did download the one from henk717 at https://github.com/KoboldAI/KoboldAI-Client but it was a little while ago.
llama
-
Mark Zuckerberg: Llama 3, $10B Models, Caesar Augustus, Bioweapons [video]
derivative works thereof).”
https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...
Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)
Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.
-
Hello OLMo: A Open LLM
One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.
-
Reaching LLaMA2 Performance with 0.1M Dollars
It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.
If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.
[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...
-
DBRX: A New Open LLM
Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.
[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65
-
How Chain-of-Thought Reasoning Helps Neural Networks Compute
This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.
LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).
But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.
The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.
This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.
There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.
They might be doing more, there is some research that represents circumstantial evidence they are doing more.
[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...
- Asking Meta to stop using the term "open source" for Llama
-
Markov Chains Are the Original Language Models
Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):
https://github.com/facebookresearch/llama/blob/main/llama/ge...
The engineering feats are up there with anything, but it’s a next token predictor.
-
Meta AI releases Code Llama 70B
https://github.com/facebookresearch/llama/pull/947/
-
Stuff we figured out about AI in 2023
> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...
Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...
and both of these are SOTA open source models.
-
[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.
What are some alternatives?
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
chatgpt-vscode - A VSCode extension that allows you to use ChatGPT
KoboldAI
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Clover-Edition - State of the art AI plays dungeon master to your adventures.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
stable-diffusion-webui - Stable Diffusion web UI
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.