Extract IsoFLOP scaling ladder data (loss values, token counts, FLOPs, params) from the Marin Delphi suite without rerunning analysis jobs.
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| """Count rows in bolinas-dna HF datasets (training + validation).""" | |
| from datasets import load_dataset | |
| DATASETS = [ | |
| ("CDS train", "bolinas-dna/genomes-v5-genome_set-animals-intervals-v5_255_128", "train"), | |
| ("Upstream train", "bolinas-dna/genomes-v5-genome_set-animals-intervals-v1_255_128", "train"), | |
| ("Downstream train", "bolinas-dna/genomes-v5-genome_set-animals-intervals-v15_255_128", "train"), | |
| ("CDS val", "bolinas-dna/genomes-v5-validation-intervals-v5_255_255", "validation"), | |
| ("Upstream val", "bolinas-dna/genomes-v5-validation-intervals-v1_255_255", "validation"), |
Prompt for artifact https://claude.ai/public/artifacts/ff4b6e45-cc20-4a96-b95c-57caac05bfff
Create an application comparing Chinchilla Approach 2 token extrapolations against a known, analytical ground truth.
All details below reference Hoffmann et al. 2022, "Training Compute-Optimal Large Language Models" (https://arxiv.org/abs/2203.15556).
Implementation:
- Define the Chinchilla loss surface: L(N, D) = E + A/N^α + B/D^β with parameters α=0.34, β=0.28, A=406.4, B=410.7, E=1.69 (Appendix D)
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| import sys, os, tempfile | |
| from huggingface_hub.utils import http_stream_backoff | |
| url = "https://cdn.mos.cms.futurecdn.net/v2/t:0,l:160,cw:960,ch:720,q:80,w:960/FaWKMJQnr2PFcYCmEyfiTm.jpg" | |
| ext = os.path.splitext(url)[1] | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as f: | |
| with http_stream_backoff( | |
| method="GET", | |
| url=url, |
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| #!/usr/bin/env python3 | |
| """ | |
| Metaflow Example: Simple bedtools sorting with Docker | |
| Demonstrates using biocontainers/bedtools via Docker SDK to sort genomic intervals. | |
| """ | |
| import tempfile | |
| import os |
This script verifies that Triton can be built and minimally tested on ARM64 systems, specifically on AWS Graviton2 instances. Instructions:
- Create a g5g.xlarge instance using the following AWS CLI command and make sure to add at least 30G storage:
aws ec2 run-instances \
--instance-type g5g.xlarge \
--image-id ami-02dcfe5d1d39baa4e \
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| ( | |
| pd.read_gbq( | |
| "SELECT pmid, EXTRACT(YEAR from pub_date) AS pub_year FROM `pmdb-bq.pmdb.article` WHERE pub_date IS NOT NULL", | |
| use_bqstorage_api=True | |
| ) | |
| .to_parquet("pub_years.parquet") | |
| ) |
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| playerID,yearID,stint,teamID,lgID,G,AB,R,H,2B,3B,HR,RBI,SB,CS,BB,SO,IBB,HBP,SH,SF,GIDP | |
| abercda01,1871,1,TRO,NA,1,4,0,0,0,0,0,0,0,0,0,0,,,,,0 | |
| addybo01,1871,1,RC1,NA,25,118,30,32,6,0,0,13,8,1,4,0,,,,,0 | |
| allisar01,1871,1,CL1,NA,29,137,28,40,4,5,0,19,3,1,2,5,,,,,1 | |
| allisdo01,1871,1,WS3,NA,27,133,28,44,10,2,2,27,1,1,0,2,,,,,0 | |
| ansonca01,1871,1,RC1,NA,25,120,29,39,11,3,0,16,6,2,2,1,,,,,0 | |
| armstbo01,1871,1,FW1,NA,12,49,9,11,2,1,0,5,0,1,0,1,,,,,0 | |
| barkeal01,1871,1,RC1,NA,1,4,0,1,0,0,0,2,0,0,1,0,,,,,0 | |
| barnero01,1871,1,BS1,NA,31,157,66,63,10,9,0,34,11,6,13,1,,,,,1 |
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