File size: 7,013 Bytes
02dd8cb
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88d4730
 
 
09b6057
 
 
 
 
 
02dd8cb
 
afb0d59
02dd8cb
afb0d59
02dd8cb
 
 
 
 
 
 
 
 
 
de09a79
02dd8cb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
09b6057
 
 
 
 
 
 
 
 
 
 
 
 
 
f43cf00
 
 
 
 
 
 
 
 
afb0d59
f43cf00
afb0d59
f43cf00
 
 
 
 
 
88d4730
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
---
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](https://www.appliedai-institute.de/en/), 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:

```python
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](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__Medium)

|      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](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-Medium)

|      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|