A comprehensive guide to running Llama 2 locally

• llama.cpp

  769 55,846 10.0 C++

  LLM inference in C/C++

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

• text-generation-webui

  876 36,293 9.9 Python

  A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

The bash script is downloading llama.cpp, a project which allows you to run LLaMA-based language models on your CPU. The bash script then downloads the 13 billion parameter GGML version of LLaMA 2. The GGML version is what will work with llama.cpp and uses CPU for inferencing. There are ways to run models using your GPU, but it depends on your setup whether it will be worth it.

I would highly recommend looking into the text-generation-webui project (https://github.com/oobabooga/text-generation-webui). It has a one-click installer and very comprehensive guides for getting models running locally and where to find models. The project also has an "api" command flag to let you use it like you might use a web-based service currently.

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• ollama

  192 58,943 9.9 Go

  Get up and running with Llama 3, Mistral, Gemma, and other large language models.

  

> adjust your paths as necessary. It has a tendency to talk to itself.

This is always a fun surprise. What I've seen help, especially with chat models, is to use a prompt template. Some tools (e.g. https://ollama.ai/ – disclosure: I'm a maintainer) use a default, model-specific prompt template when you run the model. This is easier for users since they can just input your chat messages and get answers. Every model is trained differently.

But with llama.cpp it you'd need wrap your prompt text with the right template. For llama 2, the facebook developers' generation code wraps the system prompt and user prompts in specific tags (<>{system prompt} and [INST]{user prompt}[/INST]) respectively): https://github.com/facebookresearch/llama/blob/main/llama/ge....

Worth noting that customizing prompt templates can be fun – you don't have to use them – I've had a model generate a conversation between a few characters that talk to each other for example – it's pretty entertaining!

• llama

  184 53,053 8.1 Python

  Inference code for Llama models

  

> adjust your paths as necessary. It has a tendency to talk to itself.

This is always a fun surprise. What I've seen help, especially with chat models, is to use a prompt template. Some tools (e.g. https://ollama.ai/ – disclosure: I'm a maintainer) use a default, model-specific prompt template when you run the model. This is easier for users since they can just input your chat messages and get answers. Every model is trained differently.

But with llama.cpp it you'd need wrap your prompt text with the right template. For llama 2, the facebook developers' generation code wraps the system prompt and user prompts in specific tags (<>{system prompt} and [INST]{user prompt}[/INST]) respectively): https://github.com/facebookresearch/llama/blob/main/llama/ge....

Worth noting that customizing prompt templates can be fun – you don't have to use them – I've had a model generate a conversation between a few characters that talk to each other for example – it's pretty entertaining!

• text-generation-inference

  29 7,800 9.6 Python

  Large Language Model Text Generation Inference

  

MLC LLM (iOS/Android)

Which is not really comprehensive... If you have a linux machine with GPUs, i'd just use hugging face inference (https://github.com/huggingface/text-generation-inference). And I am sure there are other things that could be covered.

• llm

  23 2,903 9.5 Python

  Access large language models from the command-line (by simonw)

My LLM command line tool can do that - it logs everything to a SQLite database and has an option to continue a conversation: https://llm.datasette.io

• blog

  5 1,989 9.8 Jupyter Notebook

  Public repo for HF blog posts (by huggingface)

  

If you just want to do inference/mess around with the model and have a 16GB GPU, then this[0] is enough to paste into a notebook. You need to have access to the HF models though.

0. https://github.com/huggingface/blog/blob/main/llama2.md#usin...

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• llama

  2 80 6.2 Python

  Inference code for LLaMA models on CPU and Mac M1/M2 GPU (by krychu)

Self-plug. Here’s a fork of the original llama 2 code adapted to run on the CPU or MPS (M1/M2 GPU) if available:

https://github.com/krychu/llama

It runs with the original weights, and gets you to ~4 tokens/sec on MacBook Pro M1 with the 7B model.

• Pytorch

  336 77,783 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

• DeepSpeed

  51 32,550 9.8 Python

  DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

  

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

• bitsandbytes

  61 5,389 9.4 Python

  Accessible large language models via k-bit quantization for PyTorch.

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

• stable-diffusion-webui

  2,808 129,299 9.9 Python

  Stable Diffusion web UI

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

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