qlora VS ggml

Marx 3B V3 is StableLM 3B 4E1T instruction tuned on EverythingLM Data V3(ShareGPT Format) for 2 epochs using QLoRA.

https://github.com/artidoro/qlora

The tools and mechanisms to get a model to do what you want is ever so changing, ever so quickly. Build and understand a notebook yourself, and reduce dependencies. You will need to switch them.

My own project right now is still in raw generated form, and this now makes me think about trying qlora's scripts since this gives me some confidence I should be able to get it to turn out now that someone else has carved a path and charted the map. I was going to target llamatune which was mentioned here the other day.

Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)

4-bit quantization training mainly refers to qlora. Simply put, qlora quantizes the weights of the LLM into 4-bit for storage, while dequantizing them into 16-bit during the training process to ensure training precision. This method significantly reduces GPU memory overhead during training (the training speed should not vary much). This approach is highly suitable to be combined with parameter-efficient methods. However, the original paper was designed for single-modal LLMs and the code has already been wrapped in HuggingFace's library. Therefore, we extracted the core code from HuggingFace's library and migrated it into LaVIN's code. The main principle is to replace all linear layers in LLM with 4-bit quantized layers. Those interested can refer to our implementation in quantization.py and mm_adaptation.py, which is roughly a dozen lines of code.

There have been some new development like QLora which help fine-tune LLMs without updating all the weights.

This might be helpful: https://github.com/artidoro/qlora/issues/10

curious, since qlora paper only reports lora/qlora comparison for full fine-tuning for small 7B models.for 13B/33B/65B, it does not do so (table 4 in paper)it would be helpful if anyone can please provide links where I can read more on efficacy of lora or disadvantages of lora?

This might not be appropriate answer but did you take a look at this repository? https://github.com/artidoro/qlora With artidoro's repository it's pretty easy to train qlora. You just prepare your own dataset and run the following command: python qlora.py --model_name_or_path --dataset="path/to/your/dataset" --dataset_format="self-instruct" This is only available for several dataset formats. But every dataset format has to have input-output pairs. So the dataset json format has to be like this [ { “input”: “something ”, “output”:“something ” }, { “input”: “something ”, “output”:“something ” } ]

git clone https://github.com/ggerganov/ggml cd ggml mkdir build cd build cmake .. make -j4 gpt-j ../examples/gpt-j/download-ggml-model.sh 6B

Cool. I was just learning about GGUF by creating my own parser for it based on the spec https://github.com/ggerganov/ggml/blob/master/docs/gguf.md (for educational purposes)

If you don't care about the details of how those model servers work, then something that abstracts out the whole process like LM Studio or Ollama is all you need.

However, if you want to get into the weeds of how this actually works, I recommend you look up model quantization and some libraries like ggml[1] that actually do that for you.

[1] https://github.com/ggerganov/ggml

Because the library is called ggml, but it supports gguf.

Apparently this guy like a bunch of others like https://github.com/ggerganov/ggml are implementing transformers from papers for people that want them. Pretty cool.

You can access it here: https://github.com/staghado/vit.cpp It has been added to the ggml library on GitHub: https://github.com/ggerganov/ggml

https://github.com/ggerganov/ggml

One note is that prompt ingestion is extremely slow on CPU compared to GPU. So short prompts are fine (as tokens can be streamed once the prompt is ingested), but long prompts feel extremely sluggish.

I did a quick run under profiler and on my AVX2-laptop the slowest part (>50%) was matrix multiplication (sgemm).

In current version of GGML if OpenBLAS is enabled, they convert matrices to FP32 before running sgemm.

If OpenBLAS is disabled, on AVX2 plaftorm they convert FP16 to FP32 on every FMA operation, which even worse (due to repetition). After that, both ggml_vec_dot_f16 and ggml_vec_dot_f32 took first place in profiler.

Source: https://github.com/ggerganov/ggml/blob/master/src/ggml.c#L10...

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/