PULI LlumiX 32K (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 = "Elmes茅lek egy t枚rt茅netet a nyelvtechnol贸gi谩r贸l."
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
print(generator(prompt, max_new_tokens=30)[0]["generated_text"])
- Downloads last month
- 799
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.