license: mit
model-index:
- name: RYS-XLarge
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 79.96
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 58.77
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 38.97
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.9
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 23.72
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.2
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-XLarge
name: Open LLM Leaderboard
This is a new kind of model optimization. This model is based on MaziyarPanahi/calme-2.1-qwen2-72b, which was tuned from Qwen2-72B.
A paper is currently being written on the technique.
Quickstart
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"dnhkng/RYS-XLarge",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("dnhkng/RYS-XLarge")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 44.75 |
| IFEval (0-Shot) | 79.96 |
| BBH (3-Shot) | 58.77 |
| MATH Lvl 5 (4-Shot) | 38.97 |
| GPQA (0-shot) | 17.90 |
| MuSR (0-shot) | 23.72 |
| MMLU-PRO (5-shot) | 49.20 |
Advertising
I’m on the hunt for new challenges and a chance to dive into some exciting research opportunities. Oh, and did I mention I just snagged a top spot on the Open LLM leaderboard? 🎉
DR DAVID NOEL NG
MACHINE LEARNING EXPERT
Innovation catalyst, AI strategist, and Interdisciplinary-Tech enthusiast – that's me in a nutshell. With over a decade of experience in research and project management, my professional journey has been largely shaped by my passion for artificial intelligence and its potential to transform various industries. With a solid background in artificial intelligence and machine learning, coupled with a knack for innovation and problem-solving (and a healthy dose of curiosity), I'm excited to bring my skills to a new team.
Originally from Australia, where I earned my degrees in Organic Chemistry and Biochemistry, I moved to Germany in 2004. My academic pursuit continued with a Ph.D. in Chemistry at the Max Planck Institute of Biochemistry. Today, I leverage my robust educational background and diverse industry experience to drive AI innovations in a wide range of applications.
PROFESSIONAL EXPERIENCE
SENIOR GLOBAL INNOVATION STRATEGIST - ARTIFICIAL INTELLIGENCE
####Munich Re | Munich | 05/2023 - Now
As a Senior Global Innovation Strategist at Munich Re, my passion is in steering AI/ML strategies, maximizing project impact, and advancing the use of cutting-edge technology. I built the AI Accelerator, which drives the rapid and structured development of AI use-case Implementations.
AI CONSULTANT - LEAD AI ENGINEER
appliedAI UTUM | Munich | 04/2019 - 04/2023
In my tenure at appliedAI, I held a leadership role where I spearheaded the successful development and execution of various AI/ML proof-of-concept (POC) and minimum viable product (MVP) projects. I utilized a hands-on approach to drive ideation, planning, and delivery of these solutions for our clients.
- AI-Controlled Imaging: Directed a PoC of an AI-Controlled Electron Microscope using Reinforcement Learning for a premier imaging company. Anomaly Detection: Oversaw development of security systems utilizing anomaly detection, integrating diverse technologies to boost client security at the Munich Security Conference..
- Project Optimization: Implemented AlphaZero-based Graph Optimization for project management in the Nuclear Energy sector.
- Food Safety: Delivered a PoC for industrial food safety equipment, significantly improving detection sensitivity.
- NLP Consulting: Consulted on automated document analysis and risk assessment for the European Central Bank, leveraging NLP technologies.
- Aerospace Anomaly Detection: Developed a PoC for Aerospace manufacturing, using generative diffusion models to create synthetic data for training anomalies detection models.
- Retail Automation: Applied Vision and Skeletal Tracking for supermarket automation, modernizing retail operations.
- Public Speaking and Training: Regularly presented talks and training sessions on topics such as KI-Transfer Plus for the Bayerischen Staatsministeriums für Digitales, and KI in Biotech for the BioEntrepreneurship Summit, spreading AI knowledge and fostering digital transformation in the Health/Pharma sector..
PROJECT LEAD - INNOVATIVE TECHNOLOGIES
Nanotemper Technologies GmbH | Munich | 5/2016 - 3/2019
Project Lead in Future Technologies Department, Scientist Bioanalytics and all-rounder in bioanalytics/data/optoelectronics. Contributions and successes:
- Created and applied Deep Learning models for interpreting biophysical data for pharmaceutical stability in antibody development
- Designed, built, and programmed prototype optoelectronic apparatus for the rapid analysis of biosimilar pharmaceutical molecules
- Introduced FPGA technology for high-speed data collection and analysis, now used in the key products at Nanotemper
RESEARCH SCIENTIST
Max Planck Institute Of Neurobiology | Martinsried | 02/2016 - 04/2019
Driven by an interest in Biotech, I found a role in research working on biosensors, particularly on optical probes of neural activity (Optogenetics). Contribution and success:
- Designed, built and utilized a robotic screening platform for the high-throughput engineering of biosensors.
- Utilised image-processing and machine-learning techniques to collect and analyse biosensor data.
- Automated the development of large molecules by FACS-based directed protein evolution.
- Patented new CRISPR/Cas9 technology for high-throughput protein engineering.
CONSULTANT FOR THE NETFLIX SERIES 'BIOHACKERS'
Netflix | Munich | 01/2019 - 12/2019
In this role, I advised on the scientific concepts, storylines and film set for this popular Netflix series. Contribution and success:
- Helped design and build the Laboratory and ‘Biohacking’ labs
- Modified the scripts to keep scientific accuracy
- Location scouting and liaison with the LMU to organise research labs for filming
SKILLS
- Strong interest in customer experience and Machine Learning transformations (e.g. expectation management, stakeholder alignment, team reorganization etc.)
- Ability to work autonomously in the completion of deliverables
- Ability to provide technical and analytic direction, guidance and roadmaps for ML projects
- Excellent communication and presentation skills: able to explain Analytics in non-technical terms to business users (C-level, investors, public presentations etc.)
- Deep technical expertise and strong problem-solving and data analysis skills
AWARDS
The United Nations COVID-19 Detect & Protect Challenge
- The United Nations Development Programme Centre for Technology, Innovation and Sustainable Development · Aug 2020
AI at the Edge Challenge with NVIDIA - Artificial Intelligence of Things (AIoT)
- Issued by Nvidia · Mar 2020
Create Intelligence at the Edge - Artificial Intelligence on FPGA
- Avnet and Xilinx · Dec 2018
PATENTS
- WO2018020050A1 - Targeted in situ protein diversification by site directed dna cleavage and repair
EDUCATION
PhD in Organic Chemistry
- Max Planck Institute of Biochemistry
Honours Degree - Biochemistry
- Monash University Melbourne
Bachelor of Science - Double Major -
- Chemistry / Molecular Biology
- University of Tasmania
Nanodegree - Deep Reinforcement Learning
- Udacity Online
Nanodegree - Deep Learning
- Udacity Online