Exllama Alternatives

exllama reviews and mentions

• Any way to optimally use GPU for faster llama calls?
  1 project | /r/LocalLLaMA | 27 Sep 2023

  not using exllama seems like the tremendous waste

• ExLlama: Memory efficient way to run Llama
  1 project | news.ycombinator.com | 15 Aug 2023
• Ask HN: Cheapest hardware to run Llama 2 70B
  5 projects | news.ycombinator.com | 9 Aug 2023
• Llama Is Expensive
  1 project | news.ycombinator.com | 20 Jul 2023

  > We serve Llama on 2 80-GB A100 GPUs, as that is the minumum required to fit Llama in memory (with 16-bit precision)

  Well there is your problem.

  LLaMA quantized to 4 bits fits in 40GB. And it gets similar throughput split between dual consumer GPUs, which likely means better throughput on a single 40GB A100 (or a cheaper 48GB Pro GPU)

  https://github.com/turboderp/exllama#dual-gpu-results

  Also, I'm not sure which model was tested, but Llama 70B chat should have better performance than the base model if the prompting syntax is right. That was only reverse engineered from the Meta demo implementation recently.

• Accessing Llama 2 from the command-line with the LLM-replicate plugin
  16 projects | news.ycombinator.com | 18 Jul 2023

  For those getting started, the easiest one click installer I've used is Nomic.ai's gpt4all: https://gpt4all.io/

  This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama.cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J models. It also has API/CLI bindings.

  I just saw a slick new tool https://ollama.ai/ that will let you install a llama2-7b with a single `ollama run llama2` command that has a very simple 1-click installer for Apple Silicon Mac (but need to build from source for anything else atm). It looks like it only supports llamas OOTB but it also seems to use llama.cpp (via Go adapter) on the backend - it seemed to be CPU-only on my MBA, but I didn't poke too much and it's brand new, so we'll see.

  For anyone on HN, they should probably be looking at https://github.com/ggerganov/llama.cpp and https://github.com/ggerganov/ggml directly. If you have a high-end Nvidia consumer card (3090/4090) I'd highly recommend looking into https://github.com/turboderp/exllama

  For those generally confused, the r/LocalLLaMA wiki is a good place to start: https://www.reddit.com/r/LocalLLaMA/wiki/guide/

  I've also been porting my own notes into a single location that tracks models, evals, and has guides focused on local models: https://llm-tracker.info/

• GPT-4 Details Leaked
  3 projects | news.ycombinator.com | 10 Jul 2023

  Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .

  If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...

• Multi-GPU questions
  1 project | /r/LocalLLaMA | 9 Jul 2023

  Exllama for example uses buffers on each card that reduce the amount of VRAM available for model and context, see here. https://github.com/turboderp/exllama/issues/121

• A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
  5 projects | /r/LocalLLaMA | 7 Jul 2023

  For inference step, this repo can help you to use ExLlama to perform inference on an evaluation dataset for the best throughput.

• GPT-4 API general availability
  15 projects | news.ycombinator.com | 6 Jul 2023

  In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.

  You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.

  That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).

  I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)

  [1] https://github.com/turboderp/exllama#results-so-far

  [2] https://github.com/aigoopy/llm-jeopardy

  [3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...

  [4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder

• Local LLMs GPUs
  2 projects | /r/LocalLLaMA | 4 Jul 2023

  That's a 16GB GPU, you should be able to fit 13B at 4bit: https://github.com/turboderp/exllama

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