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PULI GPT 3SX - GGML

Model creator: NYTK
Original model: PULI GPT 3SX

Description

This repo contains GPT-NeoX GGML format model files for NYTK's PULI GPT 3SX.

Please note that these GGMLs are not compatible with llama.cpp, text-generation-webui or llama-cpp-python. Please see below for a list of tools that work with this GGML model.

Repositories available

Prompt template: None

{prompt}

Compatibilty

These files are not compatible with llama.cpp, text-generation-webui or llama-cpp-python.

They can be used with:

KoboldCpp, a powerful inference engine based on llama.cpp with full GPU acceleration and good UI.
LM Studio, a fully featured local GUI for GGML inference on Windows and macOS.
LoLLMs-WebUI a web UI which supports nearly every backend out there. Use ctransformers backend for support for this model.
ctransformers: for use in Python code, including LangChain support.
rustformers' llm
The example gpt-neox binary provided with ggml

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Tutorial for using LoLLMs-WebUI:

Video tutorial, by LoLLMs-WebUI's author ParisNeo

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
puli-gpt-3sx.ggmlv1.q4_0.bin	q4_0	4	3.86 GB	6.36 GB	4-bit.
puli-gpt-3sx.ggmlv1.q4_1.bin	q4_1	4	4.29 GB	6.79 GB	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
puli-gpt-3sx.ggmlv1.q5_0.bin	q5_0	5	4.72 GB	7.22 GB	5-bit. Higher accuracy, higher resource usage and slower inference.
puli-gpt-3sx.ggmlv1.q5_1.bin	q5_1	5	5.15 GB	7.65 GB	5-bit. Even higher accuracy, resource usage and slower inference.
puli-gpt-3sx.ggmlv1.q8_0.bin	q8_0	8	7.29 GB	9.79 GB	8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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Thanks, and how to contribute.

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Thank you to all my generous patrons and donaters!

Original model card: NYTK's PULI GPT 3SX

PULI GPT-3SX (6.7 billion parameter)

For further details, see our demo site.

Hungarian GPT-NeoX model (6.7 billion parameter)
Trained with EleutherAI's GPT-NeoX github
Dataset: 36.3 billion words
Checkpoint: 150 000 steps

Limitations

max_seq_length = 2048

Citation

If you use this model, please cite the following paper:

@inproceedings {yang-puli,
    title = {Jönnek a nagyok! BERT-Large, GPT-2 és GPT-3 nyelvmodellek magyar nyelvre},
    booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
    year = {2023},
    publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
    address = {Szeged, Hungary},
    author = {Yang, Zijian Győző and Dodé, Réka and Ferenczi, Gergő and Héja, Enikő and Jelencsik-Mátyus, Kinga and Kőrös, Ádám and Laki, László János and Ligeti-Nagy, Noémi and Vadász, Noémi and Váradi, Tamás},
    pages = {247--262}
}

Usage

from transformers import GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPT-3SX")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPT-3SX")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids

gen_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.9,
    max_length=100,
)

gen_text = tokenizer.batch_decode(gen_tokens)[0]
print(gen_text)

Usage with pipeline

from transformers import pipeline, GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPT-3SX")
tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPT-3SX")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)

print(generator(prompt)[0]["generated_text"])

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PULI-GPT-3SX-GGML