license, language, tags

license	language	tags
llama2	hu en	puli

PULI LlumiX 32K base (6.74B billion parameter)

For further details or testing our instruct model, see our demo site.

• Trained with OpenChatKit github
• The LLaMA-2-7B-32K model were continuously pretrained on Hungarian dataset
• The model has been extended to a context length of 32K with position interpolation
• Checkpoint: 100 000 steps

Dataset for continued pretraining

• Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length
• English: Long Context QA (2 billion words), BookSum (78 million words)

Limitations

• max_seq_length = 32 768
• float16
• vocab size: 32 000

Usage with pipeline

from transformers import pipeline, LlamaForCausalLM, LlamaTokenizer

model = LlamaForCausalLM.from_pretrained("NYTK/PULI-LlumiX-32K")
tokenizer = LlamaTokenizer.from_pretrained("NYTK/PULI-LlumiX-32K")
prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)

print(generator(prompt, max_new_tokens=30)[0]["generated_text"])