FastChat
llama.cpp
FastChat | llama.cpp | |
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84 | 792 | |
35,257 | 60,282 | |
3.9% | - | |
9.5 | 10.0 | |
1 day ago | about 1 hour ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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FastChat
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MT-Bench: Comparing different LLM Judges
MT-Bench is a quick (and dirty?) way to evaluate a chatbot model (fine-tuned instruction following LLM). When a new open-source model is published at Hugging-face it is not uncommon to see the score presented as a testament of quality. It offers ~$5 worth of OpenAI API calls towards getting a good ballpark of how your model does. A good tool to iterate on fine-tuning an assistant model.
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GPT4.5 or GPT5 being tested on LMSYS?
gpt2-chatbot isn't the only "mystery model" on LMSYS. Another is "deluxe-chat".
When asked about it in October last year, LMSYS replied [0] "It is an experiment we are running currently. More details will be revealed later"
One distinguishing feature of "deluxe-chat": although it gives high quality answers, it is very slow, so slow that the arena displays a warning whenever it is invoked
[0] https://github.com/lm-sys/FastChat/issues/2527
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LLMs on your local Computer (Part 1)
FastChat
- FLaNK AI for 11 March 2024
- FLaNK 04 March 2024
- ChatGPT for Teams
- FastChat: An open platform for training and serving large language models
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LM Studio – Discover, download, and run local LLMs
How does it compare with something like FastChat? https://github.com/lm-sys/FastChat
Feature set seems like a decent amount of overlap. One limitation of FastChat, as far as I can tell, is that one is limited to the models that FastChat supports (though I think it would be minor to modify it to support arbitrary models?)
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Video-LLaVA
Looks like the Vicuna repo is Apache 2.0 also[1].
What's the interpretation of copyright law that would prevent the code being Apache 2.0 based on the source of the fine-tuning dataset?
[1] https://github.com/lm-sys/FastChat
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
Check how to start with FastChat. Support FastChat on GitHub ⭐
llama.cpp
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Apple Intelligence, the personal intelligence system
> Doing everything on-device would result in a horrible user experience. They might as well not participate in this generative AI rush at all if they hoped to keep it on-device.
On the contrary, I'm shocked over the last few months how "on device" on a Macbook Pro or Mac Studio competes plausibly with last year's early GPT-4, leveraging Llama 3 70b or Qwen2 72b.
There are surprisingly few things you "need" 128GB of so-called "unified RAM" for, but with M-series processors and the memory bandwidth, this is a use case that shines.
From this thread covering performance of llama.cpp on Apple Silicon M-series …
https://github.com/ggerganov/llama.cpp/discussions/4167
… "Buy as much memory as you can afford would be my bottom line!"
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Partial Outage on Claude.ai
I'd love to use local models, but seems like most of the easy to use software out there (LM Studio, Backyard AI, koboldcpp) doesn't really play all that nicely with my Intel Arc GPU and it's painfully slow on my Ryzen 5 4500. Even my M1 MacBook isn't that fast at generating text with even 7B models.
I wonder if llama.cpp with SYCL could help, will have to try it out: https://github.com/ggerganov/llama.cpp/blob/master/README-sy...
But even if that worked, I'd still have the problem that IDEs and whatever else I have open already eats most of the 32 GB of RAM my desktop PC has. Whereas if I ran a small code model on the MacBook and connected to it through my PC, it'd still probably be too slow for autocomplete, when compared to GitHub Copilot and less accurate than ChatGPT or Phind for most stuff.
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Why YC Went to DC
You're correct if you're focused exclusively on the work surrounding building foundation models to begin with. But if you take a broader view, having open models that we can legally fine tune and hack with locally has created a large and ever-growing community of builders and innovators that could not exist without these open models. Just take a look at projects like InvokeAI [0] in the image space or especially llama.cpp [1] in the text generation space. These projects are large, have lots of contributors, move very fast, and drive a lot of innovation and collaboration in applying AI to various domains in a way that simply wouldn't be possible without the open models.
[0] https://github.com/invoke-ai/InvokeAI
[1] https://github.com/ggerganov/llama.cpp
- Show HN: Open-Source Load Balancer for Llama.cpp
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RAG with llama.cpp and external API services
The first example will build an Embeddings database backed by llama.cpp vectorization.
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Ask HN: I have many PDFs – what is the best local way to leverage AI for search?
and at some point (https://github.com/ggerganov/llama.cpp/issues/7444)
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Deploying llama.cpp on AWS (with Troubleshooting)
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp LLAMA_CUDA=1 make -j
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Devoxx Genie Plugin : an Update
I focused on supporting Ollama, GPT4All, and LMStudio, all of which run smoothly on a Mac computer. Many of these tools are user-friendly wrappers around Llama.cpp, allowing easy model downloads and providing a REST interface to query the available models. Last week, I also added "👋🏼 Jan" support because HuggingFace has endorsed this provider out-of-the-box.
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Mistral Fine-Tune
The output of the LLM is not just one token, but a statistical distribution across all possible output tokens. The tool you use to generate output will sample from this distribution with various techniques, and you can put constraints on it like not being too repetitive. Some of them support getting very specific about the allowed output format, e.g. https://github.com/ggerganov/llama.cpp/blob/master/grammars/... So even if the LLM says that an invalid token is the most likely next token, the tool will never select it for output. It will only sample from valid tokens.
- Distributed LLM Inference with Llama.cpp
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
gpt4all - gpt4all: run open-source LLMs anywhere
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
llama-cpp-python - Python bindings for llama.cpp
ggml - Tensor library for machine learning
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM