README.md

---
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.

```python
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dnhkng__RYS-XLarge)

|      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


___________________________________

#### Reach out via messaging on HuggingFace, or via [LinkedIn](https://www.linkedin.com/in/dnhkng)