--- license: apache-2.0 language: - en datasets: - abideen/Cosmopedia-100k-pretrain metrics: - accuracy library_name: transformers --- # Model Card for Model ID # Model Name Luxeai-anu-1-bit-70M ## Model Description The Luxeai-anu-1-bit-70M Large Language Model (LLM) is my first trial to implement one-bit LLM based on the original paper - "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits". I have taken the pre-trained Mistral-7B-v0.3 and abideen/Cosmopedia-100k-pretrain dataset. I used Microsoft Azure Standard_NC6s_v3 6 cores, 112GB RAM, 736GB storage 1 x NVIDIA Tesla V100 to train this initial model. I will be training on a much bigger dataset once I get a sponshorship for a 8x DGX System. I have tested on a sub-set of the same dataset. ## Intended Use - **Task**: text generation ## How to Use Please follow the below code to run and test it in Python Jupyter Notebook ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline from transformers.models.llama.modeling_llama import * # Load the model model = "arunb74/Luxeai-anu-1-bit-70M" tokenizer = AutoTokenizer.from_pretrained(model) model = AutoModelForCausalLM.from_pretrained(model) # Create a text generation pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, device_map="auto" ) prompt = "The LISA Pathfinder scientific collaboration will meet in Trento" sequences = pipe( f"[INST] {prompt} [/INST]", do_sample=True, max_new_tokens=100, temperature=0.7, top_k=50, top_p=0.95, num_return_sequences=1, ) print(sequences[0]['generated_text']) """ The output will be as follows - [INST] The LISA Pathfinder scientific collaboration will meet in Trento [/INST] The LISA Pathfinder Biology, a leading provider of biochemistry and molecular biology, provides a comprehensive understanding of the mechanisms and mechanisms of the LISA pathways. The LISA Pathfinder Biology, a researcher specializing in molecular biology, is a clinical trial of the disease, and its pathophysiology, and a combination of the most commonly used and widely used treatments. It is a relatively simple procedure that involves two steps. # I need community members to help me further for feedback, suitable dataset for further training, testing, evaluation. """