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README_GPU.md

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GPU Details

Hugging Face type models and LLaMa.cpp models are supported via CUDA on linux and via MPS on MACOS.

To run in ChatBot mode using bitsandbytes in 8-bit, do:

python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b --load_8bit=True

Then point browser at http://0.0.0.0:7860 (linux) or http://localhost:7860 (windows/mac) or the public live URL printed by the server (disable shared link with --share=False). For 4-bit or 8-bit support, older GPUs may require older bitsandbytes installed as pip uninstall bitsandbytes -y ; pip install bitsandbytes==0.38.1. For production uses, we recommend at least the 12B model, ran as:

python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --load_8bit=True

and one can use --h2ocolors=False to get soft blue-gray colors instead of H2O.ai colors. Here is a list of environment variables that can control some things in generate.py.

Note if you download the model yourself and point --base_model to that location, you'll need to specify the prompt_type as well by running:

python generate.py --base_model=<user path> --load_8bit=True --prompt_type=human_bot

for some user path <user path> and the prompt_type must match the model or a new version created in prompter.py or added in UI/CLI via prompt_dict.

For quickly using a private document collection for Q/A, place documents (PDFs, text, etc.) into a folder called user_path and run

python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b  --load_8bit=True --langchain_mode=UserData --user_path=user_path

For more details about document Q/A, see LangChain Readme.

For 4-bit support, when running generate pass --load_4bit=True, which is only supported for certain architectures like GPT-NeoX-20B, GPT-J, LLaMa, etc.

Any other instruct-tuned base models can be used, including non-h2oGPT ones. Larger models require more GPU memory.

AutoGPTQ

Important: If run below commands and see CUDA extension not installed mentioned during loading of model, need to recompile, because, otherwise, the generation will be much slower even if it uses GPU.

An example with AutoGPTQ is:

python generate.py --base_model=TheBloke/Nous-Hermes-13B-GPTQ --score_model=None --load_gptq=nous-hermes-13b-GPTQ-4bit-128g.no-act.order --use_safetensors=True --prompt_type=instruct --langchain_mode=UserData

This will use about 9800MB. You can also add --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 to save some memory on embedding to reach 9340MB.

For LLaMa2 70B model quantized in 4-bit AutoGPTQ, can run:

CUDA_VISIBLE_DEVICES=0 python generate.py --base_model=Llama-2-70B-chat-GPTQ --load_gptq="gptq_model-4bit--1g" --use_safetensors=True --prompt_type=llama2 --save_dir='save`

which gives about 12 tokens/sec. For 7b run:

python generate.py --base_model=TheBloke/Llama-2-7b-Chat-GPTQ --load_gptq="gptq_model-4bit-128g" --use_safetensors=True --prompt_type=llama2 --save_dir='save`

For full 16-bit with 16k context across all GPUs:

pip install transformers==4.31.0  # breaks load_in_8bit=True in some cases (https://github.com/huggingface/transformers/issues/25026)
python generate.py --base_model=meta-llama/Llama-2-70b-chat-hf --prompt_type=llama2 --rope_scaling="{'type': 'linear', 'factor': 4}" --use_gpu_id=False --save_dir=savemeta70b

and running on 4xA6000 gives about 4tokens/sec consuming about 35GB per GPU of 4 GPUs when idle. Or for GPTQ with RoPE:

pip install transformers==4.31.0  # breaks load_in_8bit=True in some cases (https://github.com/huggingface/transformers/issues/25026)
python generate.py --base_model=TheBloke/Llama-2-7b-Chat-GPTQ --load_gptq="gptq_model-4bit-128g" --use_safetensors=True --prompt_type=llama2 --score_model=None --save_dir='7bgptqrope4` --rope_scaling="{'type':'dynamic', 'factor':4}"
--max_max_new_tokens=15000 --max_new_tokens=15000 --max_time=12000

for which the GPU only uses 5.5GB. One can add (e.g.) --min_new_tokens=4096 to force generation to continue beyond model's training norms, although this may give lower quality responses. Currently, Hugging Face transformers does not support GPTQ directly except in text-generation-inference (TGI) server, but TGI does not support RoPE scaling. Also, vLLM supports LLaMa2 and AutoGPTQ but not RoPE scaling. Only exllama supports AutoGPTQ with RoPE scaling.

exllama

Currently, only exllama supports AutoGPTQ with RoPE scaling. To run RoPE scaling the LLaMa-2 7B model for 16k context:

python generate.py --base_model=TheBloke/Llama-2-7b-Chat-GPTQ --load_gptq="gptq_model-4bit-128g" --use_safetensors=True --prompt_type=llama2 --save_dir='save' --load_exllama=True --revision=gptq-4bit-32g-actorder_True --rope_scaling="{'alpha_value':4}"

which shows how to control alpha_value and the revision for a given model on TheBloke/Llama-2-7b-Chat-GPTQ. Be careful as setting alpha_value higher consumes substantially more GPU memory. Also, some models have wrong config values for max_position_embeddings or max_sequence_length, and we try to fix those for LLaMa2 if llama-2 appears in the lower-case version of the model name. Another type of model is

python generate.py --base_model=TheBloke/Nous-Hermes-Llama2-GPTQ --load_gptq="gptq_model-4bit-128g" --use_safetensors=True --prompt_type=llama2 --save_dir='save' --load_exllama=True --revision=gptq-4bit-32g-actorder_True --rope_scaling="{'alpha_value':4}"

and note the different prompt_type. For LLaMa2 70B run:

python generate.py --base_model=TheBloke/Llama-2-70B-chat-GPTQ --load_gptq=gptq_model-4bit-128g --use_safetensors=True --prompt_type=llama2 --load_exllama=True --revision=main

which uses about 48GB of memory on 1 GPU and runs at about 12 tokens/second on an A6000, which is about half the speed of 16-bit if run that on 2*A100 GPUs.

With exllama, ensure --concurrency_count=1 else the model will share states and mix-up concurrent requests.

For LLaMa.cpp on GPU run:
python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path

and ensure output shows:

ggml_init_cublas: found 2 CUDA devices:
  Device 0: NVIDIA GeForce RTX 3090 Ti
  Device 1: NVIDIA GeForce RTX 2080
llama.cpp: loading model from llama-2-7b-chat.ggmlv3.q8_0.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32001
llama_model_load_internal: n_ctx      = 1792
llama_model_load_internal: n_embd     = 4096
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 32
llama_model_load_internal: n_layer    = 32
llama_model_load_internal: n_rot      = 128
llama_model_load_internal: ftype      = 7 (mostly Q8_0)
llama_model_load_internal: n_ff       = 11008
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size =    0.08 MB
llama_model_load_internal: using CUDA for GPU acceleration
ggml_cuda_set_main_device: using device 0 (NVIDIA GeForce RTX 3090 Ti) as main device
llama_model_load_internal: mem required  = 4518.85 MB (+ 1026.00 MB per state)
llama_model_load_internal: allocating batch_size x (512 kB + n_ctx x 128 B) = 368 MB VRAM for the scratch buffer
llama_model_load_internal: offloading 20 repeating layers to GPU
llama_model_load_internal: offloaded 20/35 layers to GPU
llama_model_load_internal: total VRAM used: 4470 MB
llama_new_context_with_model: kv self size  =  896.00 MB