README.md

---
license: mit
model-index:
- name: RYS-Phi-3-medium-4k-instruct
  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: 43.91
      name: strict accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      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: 46.75
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      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: 11.78
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      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: 13.98
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      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: 11.09
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      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: 42.74
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/RYS-Phi-3-medium-4k-instruct
      name: Open LLM Leaderboard
---

This is a new kind of model optimization.  This model is based on Microsoft's Phi-3-medium-4k-instruct.

A paper on the technique is currently being written.

This research was supported with hardware from the [appliedAI Institute](https://www.appliedai-institute.de/en/), whose goal is to generate and communicate high-quality knowledge about trustworthy AI.

#### Usgae with Transformers AutoModelForCausalLM
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)
model_id = "dnhkng/RYS-Phi-3-medium-4k-instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```


___________________________________
# *ADVERTISING BREAK*

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? 🎉



#### Profile
Innovation enthusiast, AI strategist, and interdisciplinary-tech nerd – that's me! 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 PhD 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. Hobbies? Lots: I've also built the world's most powerful espresso machine and am working to bring [GLaDOS to life](https://github.com/dnhkng/GlaDOS).


___________________________________
I'm based out of Munich, Germany, but I would be interested in working remotely for a team with more compute than my 2x 4090s 🚀

#### Reach out via [LinkedIn - Dr David Noel Ng](https://www.linkedin.com/in/dnhkng)

# [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-Phi-3-medium-4k-instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |28.38|
|IFEval (0-Shot)    |43.91|
|BBH (3-Shot)       |46.75|
|MATH Lvl 5 (4-Shot)|11.78|
|GPQA (0-shot)      |13.98|
|MuSR (0-shot)      |11.09|
|MMLU-PRO (5-shot)  |42.74|