bigsnarfdude

• summarized [[201809291404-Ghoussoub]]

• [[summary]]

  • The speaker expresses gratitude to the scientific community in Canada for their respect and admiration, highlighting the importance of material science due to the " acceleration in the mathematization of all aspects of the sciences, but also of society. "

  • The speaker explains the rationale behind BIRS (Banff International Research Station), emphasizing the goal to " multiply the opportunities " and " democratize " access to talent and opportunities in mathematics and related fields.

  • BIRS aims to facilitate interaction among scientists, described as " getting people together to live together for a week, eat and drink, and sleep mathematics if they can, and do mathematics together collaboratively. "

• The speaker notes the historical context of BIRS, mentioning that the first institute of this form started in Germany post-World War II and that BIRS was inspired by similar models in Europe, such as one near Marseille, Fra

bigsnarfdude

bigsnarfdude

https://health-infobase.canada.ca/wastewater/

https://www.genome.gov/genetics-glossary/Base-Pair

https://www.youtube.com/watch?v=GqTBKDzPYmg

https://research.ucalgary.ca/acwa/acwa

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bigsnarfdude

BIRS plan focused on processing BIRS existing 16000 video archive first, then setting up the continuous system. This is a good approach since it separates the bulk historical processing from the real-time system. Archive Processing Phase:

Create a Video Processing Pipeline:

Build a parallel processing system using Python's multiprocessing or distributed computing with Dask to handle the 16,000 videos efficiently Set up error handling and logging to track failed transcriptions and allow for easy retries Store video metadata (paths, durations, processing status) in SQLite or PostgreSQL for tracking

bigsnarfdude

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1")
model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", torch_dtype=torch.bfloat16)

# Example input sequence
input_sequence = "TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTC"

bigsnarfdude

from unsloth import FastLanguageModel, is_bfloat16_supported
from unsloth.chat_templates import get_chat_template, train_on_responses_only
from datasets import Dataset
from trl import SFTTrainer
from transformers import TrainingArguments, DataCollatorForSeq2Seq
import torch
import wandb
from datetime import datetime
import json

bigsnarfdude

from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
from transformers import TextStreamer
from peft import PeftModel
import torch
import re

def load_model(model_path):
    """Load the fine-tuned LoRA model and tokenizer"""
    # Initialize base model and tokenizer

bigsnarfdude

from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
from transformers import TextStreamer
from peft import PeftModel
import torch

def load_model(model_path):
    """Load the fine-tuned LoRA model and tokenizer"""
    # Initialize base model and tokenizer
    base_model, tokenizer = FastLanguageModel.from_pretrained(

bigsnarfdude

from unsloth import FastLanguageModel, is_bfloat16_supported
from unsloth.chat_templates import get_chat_template, standardize_sharegpt, train_on_responses_only
from datasets import Dataset
from trl import SFTTrainer
from transformers import TrainingArguments, DataCollatorForSeq2Seq, TextStreamer
import torch
import wandb
from datetime import datetime
import json

bigsnarfdude

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

class RegulationMatcher:
    def __init__(self):
        self.examples = []
        self.vectorizer = TfidfVectorizer()
        
    def add_example(self, statement, regulation):
        """Add a training example to the system."""