license: mit
model-index:
- name: Medium
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: 44.06
name: strict accuracy
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 44.06
name: strict accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
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: 47.73
name: normalized accuracy
- type: acc_norm
value: 47.73
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
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: 7.78
name: exact match
- type: exact_match
value: 7.78
name: exact match
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
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: 10.4
name: acc_norm
- type: acc_norm
value: 10.4
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
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: 8.73
name: acc_norm
- type: acc_norm
value: 8.73
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
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: 36.96
name: accuracy
- type: acc
value: 36.96
name: accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=dnhkng/Medium
name: Open LLM Leaderboard
This is a new kind of model optimization. A paper on the technique is currently being written.
This research was supported with hardware from the appliedAI Institute, whose goal is to generate and communicate high-quality knowledge about trustworthy AI.
Quickstart
This code snippets show how to get quickly started with running the model on a GPU:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model_id = "dnhkng/Medium"
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'])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.94 |
| IFEval (0-Shot) | 44.06 |
| BBH (3-Shot) | 47.73 |
| MATH Lvl 5 (4-Shot) | 7.78 |
| GPQA (0-shot) | 10.40 |
| MuSR (0-shot) | 8.73 |
| MMLU-PRO (5-shot) | 36.96 |
SHAMELESS 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.
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
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.94 |
| IFEval (0-Shot) | 44.06 |
| BBH (3-Shot) | 47.73 |
| MATH Lvl 5 (4-Shot) | 7.78 |
| GPQA (0-shot) | 10.40 |
| MuSR (0-shot) | 8.73 |
| MMLU-PRO (5-shot) | 36.96 |