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Frequently asked questions

Other models

One can choose any huggingface model.

Just pass the name after --base_model=, but a prompt_type is required if we don't already have support. E.g. for vicuna models, a typical prompt_type is used and we support that already automatically for specific models, but if you pass --prompt_type=instruct_vicuna with any other Vicuna model, we'll use it assuming that is the correct prompt type. See models that are currently supported in this automatic way, and the same dictionary shows which prompt types are supported: prompter.

Low-memory mode

For GPU case, a reasonable model for low memory is to run:

python generate.py --base_model=h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 --score_model=None --load_8bit=True --langchain_mode='UserData'

which uses good but smaller base model, embedding model, and no response score model to save GPU memory. If you can do 4-bit, then do:

python generate.py --base_model=h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 --score_model=None --load_4bit=True --langchain_mode='UserData'

This uses 5800MB to startup, then soon drops to 5075MB after torch cache is cleared. Asking a simple question uses up to 6050MB. Adding a document uses no more new GPU memory. Asking a question uses up to 6312MB for a few chunks (default), then drops back down to 5600MB.

On CPU case, a good model that's still low memory is to run:

python generate.py --base_model='llama' --prompt_type=wizard2 --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 --langchain_mode=UserData --user_path=user_path

ValueError: ...offload....

The current `device_map` had weights offloaded to the disk. Please provide an `offload_folder` for them. Alternatively, make sure you have `safetensors` installed if the model you are using offers
the weights in this format.

If you see this error, then you either have insufficient GPU memory or insufficient CPU memory. E.g. for 6.9B model one needs minimum of 27GB free memory.

TypeError: Chroma.init() got an unexpected keyword argument 'anonymized_telemetry'

Please check your version of langchain vs. the one in requirements.txt. Somehow the wrong version is installed. Try to install the correct one.

Larger models require more GPU memory

Depending on available GPU memory, you can load differently sized models. For multiple GPUs, automatic sharding can be enabled with --use_gpu_id=False, but this is disabled by default since cuda:x cuda:y mismatches can occur.

For GPUs with at least 24GB of memory, we recommend:

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

or

python generate.py --base_model=h2oai/h2ogpt-oasst1-512-20b --load_8bit=True

For GPUs with at least 48GB of memory, we recommend:

python generate.py --base_model=h2oai/h2ogpt-oasst1-512-20b --load_8bit=True

etc.

CPU with no AVX2 or using LLaMa.cpp

For GPT4All based models, require AVX2, unless one recompiles that project on your system. Until then, use llama.cpp models instead.

So we recommend downloading models from TheBloke that are version 3 quantized ggml files to work with latest llama.cpp. See main README.md.

The following example is for the base LLaMa model, not instruct-tuned, so it is not recommended for chatting. It just gives an example of how to quantize if you are an expert.

Compile the llama model on your system by following the instructions and llama-cpp-python, e.g. for Linux:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make clean
make LLAMA_OPENBLAS=1

on CPU, or for GPU:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make clean
make LLAMA_CUBLAS=1

etc. following different scenarios.

Then:

# obtain the original LLaMA model weights and place them in ./models, i.e. models should contain:
# 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model

# install Python dependencies
conda create -n llamacpp -y
conda activate llamacpp
conda install python=3.10 -y
pip install -r requirements.txt

# convert the 7B model to ggml FP16 format
python convert.py models/7B/

# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0

# test by running the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128

then adding an entry in the .env_gpt4all file like (assumes version 3 quantization)

# model path and model_kwargs
model_path_llama=./models/7B/ggml-model-q4_0.bin

or wherever you placed the model with the path pointing to wherever the files are located (e.g. link from h2oGPT repo to llama.cpp repo folder), e.g.

cd ~/h2ogpt/
ln -s ~/llama.cpp/models/* .

then run h2oGPT like:

python generate.py --base_model='llama' --langchain_mode=UserData --user_path=user_path

Is this really a GGML file? Or Using version 2 quantization files from GPT4All that are LLaMa based

If hit error:

Found model file.
llama.cpp: loading model from ./models/7B/ggml-model-q4_0.bin
error loading model: unknown (magic, version) combination: 67676a74, 00000003; is this really a GGML file?
llama_init_from_file: failed to load model
LLAMA ERROR: failed to load model from ./models/7B/ggml-model-q4_0.bin

then note that llama.cpp upgraded to version 3, and we use llama-cpp-python version that supports only that latest version 3. GPT4All does not support version 3 yet. If you want to support older version 2 llama quantized models, then do:

pip install --force-reinstall --ignore-installed --no-cache-dir llama-cpp-python==0.1.48

to go back to the prior version. Or specify the model using GPT4All as --base_model='gpt4all_llama and ensure entry exists like:

model_path_gpt4all_llama=./models/7B/ggml-model-q4_0.bin

assuming that file is from version 2 quantization.

I get the error: The model 'OptimizedModule' is not supported for . Supported models are ...

This warning can be safely ignored.

What ENVs can I pass to control h2oGPT?

  • SAVE_DIR: Local directory to save logs to,
  • ADMIN_PASS: Password to access system info, logs, or push to aws s3 bucket,
  • AWS_BUCKET: AWS bucket name to push logs to when have admin access,
  • AWS_SERVER_PUBLIC_KEY: AWS public key for pushing logs to when have admin access,
  • AWS_SERVER_SECRET_KEY: AWS secret key for pushing logs to when have admin access,
  • HUGGINGFACE_API_TOKEN: Read or write HF token for accessing private models,
  • LANGCHAIN_MODE: LangChain mode, overrides CLI,
  • SCORE_MODEL: HF model to use for scoring prompt-response pairs, None for no scoring of responses,
  • HEIGHT: Height of Chat window,
  • allow_upload_to_user_data: Whether to allow uploading to Shared UserData,
  • allow_upload_to_my_data: Whether to allow uploading to Scratch MyData,
  • HEIGHT: Height of Chat window,
  • HUGGINGFACE_SPACES: Whether on public A10G 24GB HF spaces, sets some low-GPU-memory defaults for public access to avoid GPU memory abuse by model switching, etc.
  • HF_HOSTNAME: Name of HF spaces for purpose of naming log files,
  • GPT_H2O_AI: Whether on public 48GB+ GPU instance, sets some defaults for public access to avoid GPU memory abuse by model switching, etc.,
  • CONCURRENCY_COUNT: Number of concurrency users to gradio server (1 is fastest since LLMs tend to consume all GPU cores, but 2-4 is best to avoid any single user waiting too long to get response)
  • API_OPEN: Whether API access is visible,
  • ALLOW_API: Whether to allow API access,
  • CUDA_VISIBLE_DEVICES: Standard list of CUDA devices to make visible.
  • PING_GPU: ping GPU every few minutes for full GPU memory usage by torch, useful for debugging OOMs or memory leaks
  • GET_GITHASH: get git hash on startup for system info. Avoided normally as can fail with extra messages in output for CLI mode

These can be useful on HuggingFace spaces, where one sets secret tokens because CLI options cannot be used.

GPT4All not producing output.

Please contact GPT4All team. Even a basic test can give empty result.

>>> from gpt4all import GPT4All as GPT4AllModel
>>> m = GPT4AllModel('ggml-gpt4all-j-v1.3-groovy.bin')
Found model file.
gptj_model_load: loading model from '/home/jon/.cache/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin' - please wait ...
gptj_model_load: n_vocab = 50400
gptj_model_load: n_ctx   = 2048
gptj_model_load: n_embd  = 4096
gptj_model_load: n_head  = 16
gptj_model_load: n_layer = 28
gptj_model_load: n_rot   = 64
gptj_model_load: f16     = 2
gptj_model_load: ggml ctx size = 5401.45 MB
gptj_model_load: kv self size  =  896.00 MB
gptj_model_load: ................................... done
gptj_model_load: model size =  3609.38 MB / num tensors = 285
>>> m.generate('Was Avogadro a  professor at the University of Turin?')

''
>>>

Also, the model tends to not do well when input has new lines, spaces or <br> work better. This does not seem to be an issue with h2oGPT.

Commercial viability

Open-source means the models are not proprietary and are available to download. In addition, the license for all of our non-research models is Apache V2, which is a fully permissive license. Some licenses for other open-source models are not fully permissive, such as StabilityAI's models that are CC-BY-SA that require derivatives to be shared too.

We post models and license and data origin details on our huggingface page: https://huggingface.co/h2oai (all models, except research ones, are fully permissive). The foundational models we fine-tuned on, e.g. Pythia 6.9B, Pythia 12B, NeoX 20B, or Open-LLaMa checkpoints are fully commercially viable. These foundational models are also listed on the huggingface page for each fine-tuned model. Full training logs, source data, etc. are all provided for all models. GPT4All GPT_J is commercially viable, but other models may not be. Any Meta based LLaMa based models are not commercially viable.

Data used to fine-tune are provided on the huggingface pages for each model. Data for foundational models are provided on their huggingface pages. Any models trained on GPT3.5 data like ShareGPT, Vicuna, Alpaca, etc. are not commercially viable due to ToS violations w.r.t. building competitive models. Any research-based h2oGPT models based upon Meta's weights for LLaMa are not commercially viable.

Overall, we have done a significant amount of due diligence regarding data and model licenses to carefully select only fully permissive data and models for our models we license as Apache V2. Outside our models, some "open-source" models like Vicuna, Koala, WizardLM, etc. are based upon Meta's weights for LLaMa, which is not commercially usable due to ToS violations w.r.t. non-competitive clauses well as research-only clauses. Such models tend to also use data from GPT3.5 (ChatGPT), which is also not commercially usable due to ToS violations w.r.t. non-competitive clauses. E.g. Alpaca data, ShareGPT data, WizardLM data, etc. all fall under that category. All open-source foundational models consume data from the internet, including the Pile or C4 (web crawl) that may contain objectionable material. Future licenses w.r.t. new web crawls may change, but it is our understanding that existing data crawls would not be affected by any new licenses. However, some web crawl data may contain pirated books.

Disclaimers

Disclaimers and a ToS link are displayed to protect the app creators.

What are the different prompt types? How does prompt engineering work for h2oGPT?

In general, all LLMs use strings as inputs for training/fine-tuning and generation/inference. To manage a variety of possible language task types, we divide any such string into the following three parts:

  • Instruction
  • Input
  • Response

Each of these three parts can be empty or non-empty strings, such as titles or newlines. In the end, all of these prompt parts are concatenated into one string. The magic is in the content of those substrings. This is called prompt engineering.

Summarization

For training a summarization task, we concatenate these three parts together:

  • Instruction = <INSTRUCTION>
  • Input = '## Main Text\n\n' + <INPUT>
  • Response = '\n\n## Summary\n\n' + <OUTPUT>

For each training record, we take <INPUT> and <OUTPUT> from the summarization dataset (typically two fields/columns), place them into the appropriate position, and turn that record into one long string that the model can be trained with: '## Main Text\n\nLarge Language Models are Useful.\n\n## Summary\n\nLLMs rock.'

At inference time, we will take the <INPUT> only and stop right after '\n\n## Summary\n\n' and the model will generate the summary as the continuation of the prompt.

ChatBot

For a conversational chatbot use case, we use the following three parts:

  • Instruction = <INSTRUCTION>
  • Input = '<human>: ' + <INPUT>
  • Response = '<bot>: ' + <OUTPUT>

And a training string could look like this: '<human>: hi, how are you?<bot>: Hi, I am doing great. How can I help you?'. At inference time, the model input would be like this: '<human>: Tell me a joke about snow flakes.<bot>: ', and the model would generate the bot part.

How should training data be prepared?

Training data (in JSON format) must contain at least one column that maps to instruction, input or output. Their content will be placed into the <INSTRUCTION>, <INPUT>, and <OUTPUT> placeholders mentioned above. The chosen prompt_type will fill in the strings in between to form the actual input into the model. Any missing columns will lead to empty strings. Optional --data_col_dict={'A': 'input', 'B': 'output'} argument can be used to map different column names into the required ones.

Examples

The following are examples of training records in JSON format.

  • human_bot prompt type
{
  "input": "Who are you?",
  "output": "My name is h2oGPT.",
  "prompt_type": "human_bot"
}
  • plain version of human_bot, useful for longer conversations
{
  "input": "<human>: Who are you?\n<bot>: My name is h2oGPT.\n<human>: Can you write a poem about horses?\n<bot>: Yes, of course. Here it goes...",
  "prompt_type": "plain"
}
  • summarize prompt type
{
  "instruction": "",
  "input": "Long long long text.",
  "output": "text.",
  "prompt_type": "summarize"
}

Context length

Note that the total length of the text (that is, the input and output) the LLM can handle is limited by the so-called context length. For our current models, the context length is 2048 tokens. Longer context lengths are computationally more expensive due to the interactions between all tokens in the sequence. A context length of 2048 means that for an input of, for example, 1900 tokens, the model will be able to create no more than 148 new tokens as part of the output.

For fine-tuning, if the average length of inputs is less than the context length, one can provide a cutoff_len of less than the context length to truncate inputs to this amount of tokens. For most instruction-type datasets, a cutoff length of 512 seems reasonable and provides nice memory and time savings. For example, the h2oai/h2ogpt-oasst1-512-20b model was trained with a cutoff length of 512.

Tokens

The following are some example tokens (from a total of ~50k), each of which is assigned a number:

"osed": 1700,
"ised": 1701,
"================": 1702,
"ED": 1703,
"sec": 1704,
"Ġcome": 1705,
"34": 1706,
"ĠThere": 1707,
"Ġlight": 1708,
"Ġassoci": 1709,
"gram": 1710,
"Ġold": 1711,
"Ġ{#": 1712,

The model is trained with these specific numbers, so the tokenizer must be kept the same for training and inference/generation. The input format doesn't change whether the model is in pretraining, fine-tuning, or inference mode, but the text itself can change slightly for better results, and that's called prompt engineering.

Is h2oGPT multilingual?

Yes. Try it in your preferred language.

What does 512 mean in the model name?

The number 512 in the model names indicates the cutoff lengths (in tokens) used for fine-tuning. Shorter values generally result in faster training and more focus on the last part of the provided input text (consisting of prompt and answer).

Throttle GPUs in case of reset/reboot

(h2ogpt) jon@gpu:~$ sudo nvidia-smi -pl 250
Power limit for GPU 00000000:3B:00.0 was set to 250.00 W from 300.00 W.
Power limit for GPU 00000000:5E:00.0 was set to 250.00 W from 300.00 W.
Power limit for GPU 00000000:86:00.0 was set to 250.00 W from 300.00 W.
Power limit for GPU 00000000:AF:00.0 was set to 250.00 W from 300.00 W.
All done.

Heterogeneous GPU systems

In case you get peer-to-peer related errors on non-homogeneous GPU systems, set this env var:

export NCCL_P2P_LEVEL=LOC

Use Wiki data

The following example demonstrates how to use Wiki data:

>>> from datasets import load_dataset
>>> wk = load_dataset("wikipedia", "20220301.en")
>>> wk
DatasetDict({
    train: Dataset({
        features: ['id', 'url', 'title', 'text'],
        num_rows: 6458670
    })
})
>>> sentences = ".".join(wk['train'][0]['text'].split('.')[0:2])
'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful'
>>>

Centos with llama-cpp-python

This may help to get llama-cpp-python to install

# remove old gcc
yum remove gcc yum remove gdb
# install scl-utils
sudo yum install scl-utils sudo yum install centos-release-scl
# find devtoolset-11
yum list all --enablerepo='centos-sclo-rh' | grep "devtoolset"
# install devtoolset-11-toolchain
yum install -y devtoolset-11-toolchain
# add gcc 11 to PATH by adding following script to /etc/profile
PATH=$PATH::/opt/rh/devtoolset-11/root/usr/bin export PATH sudo scl enable devtoolset-11 bash
# show gcc version and gcc11 is installed successfully.
gcc --version
export FORCE_CMAKE=1
export CMAKE_ARGS=-DLLAMA_OPENBLAS=on
pip install llama-cpp-python --no-cache-dir