File size: 12,496 Bytes
67efbea
 
 
 
 
 
 
 
 
 
 
 
 
 
508b89b
 
 
 
 
 
 
 
 
 
67efbea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
43a6d81
67efbea
47562d5
67efbea
47562d5
 
0033cff
47562d5
 
 
 
 
0033cff
47562d5
 
 
0033cff
67efbea
 
43a6d81
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
license: other
license_name: apple-sample-code-license
license_link: LICENSE
---

# OpenELM

*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*

We introduce **OpenELM**, a family of **Open**-source **E**fficient **L**anguage **M**odels. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.  

Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. 

See the list below for the details of each model:

- [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)                   
- [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)                   
- [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)                   
- [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)                       
- [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) 
- [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) 
- [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) 
- [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)     


```python

from transformers import AutoModelForCausalLM

openelm_270m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M", trust_remote_code=True)
openelm_450m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M", trust_remote_code=True)
openelm_1b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B", trust_remote_code=True)
openelm_3b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B", trust_remote_code=True)

openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True)
openelm_450m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M-Instruct", trust_remote_code=True)
openelm_1b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B-Instruct", trust_remote_code=True)
openelm_3b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B-Instruct", trust_remote_code=True)

```

## Usage

We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`.

You can try the model by running the following command:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
```
Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.

Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
```
Alternatively, model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) can be also tried by passing a smaller model model through the `assistant_model` argument, for example:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL_NAME]
```


## Main Results

### Zero-Shot

| **Model Size**                                                              | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA**  | **SciQ**  | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------|-----------|---------------|-----------|-----------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)                   | 26.45     | 45.08     | **53.98** | 46.71         | 69.75     | **84.70** | **53.91**      | 54.37       |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **30.55** | **46.68** | 48.56     | **52.07**     | **70.78** | 84.40     | 52.72          | **55.11**   |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)                   | 27.56     | 48.06     | 55.78     | 53.97         | 72.31     | 87.20     | 58.01          | 57.56       |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **30.38** | **50.00** | **60.37** | **59.34**     | **72.63** | **88.00** | **58.96**      | **59.95**   |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)                   | 32.34     | **55.43** | 63.58     | 64.81         | **75.57** | **90.60** | 61.72          | 63.44       |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **37.97** | 52.23     | **70.00** | **71.20**     | 75.03     | 89.30     | **62.75**      | **65.50**   |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)                       | 35.58     | 59.89     | 67.40     | 72.44         | 78.24     | **92.70** | 65.51          | 67.39       |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)     | **39.42** | **61.74** | **68.17** | **76.36**     | **79.00** | 92.50     | **66.85**      | **69.15**   |

### LLM360

| **Model Size**                                                              | **ARC-c** | **HellaSwag** | **MMLU**  | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|---------------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)                   | 27.65     | 47.15         | 25.72     | **39.24**      | **53.83**      | 38.72       |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | **51.58**     | **26.70** | 38.72          | 53.20          | **40.54**   |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)                   | 30.20     | 53.86         | **26.01** | 40.18          | 57.22          | 41.50       |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | **59.31**     | 25.41     | **40.48**      | **58.33**      | **43.41**   |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)                   | 36.69     | 65.71         | **27.05** | 36.98          | 63.22          | 45.93       |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | **71.83**     | 25.65     | **45.95**      | **64.72**      | **49.94**   |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)                       | 42.24     | 73.28         | **26.76** | 34.98          | 67.25          | 48.90       |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)     | **47.70** | **76.87**     | 24.80     | **38.76**      | **67.96**      | **51.22**   |


### OpenLLM Leaderboard

| **Model Size**                                                              | **ARC-c** | **CrowS-Pairs** | **HellaSwag** | **MMLU**  | **PIQA**  | **RACE**  | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------------|---------------|-----------|-----------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)                   | 27.65     | **66.79**       | 47.15         | 25.72     | 69.75     | 30.91     | **39.24**      | **53.83**      | 45.13       |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01           | **51.58**     | **26.70** | **70.78** | 33.78     | 38.72          | 53.20          | **46.66**   |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)                   | 30.20     | **68.63**       | 53.86         | **26.01** | 72.31     | 33.11     | 40.18          | 57.22          | 47.69       |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44           | **59.31**     | 25.41     | **72.63** | **36.84** | **40.48**      | **58.33**      | **49.25**   |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)                   | 36.69     | **71.74**       | 65.71         | **27.05** | **75.57** | 36.46     | 36.98          | 63.22          | 51.68       |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02           | **71.83**     | 25.65     | 75.03     | **39.43** | **45.95**      | **64.72**      | **54.40**   |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)                       | 42.24     | **73.29**       | 73.28         | **26.76** | 78.24     | **38.76** | 34.98          | 67.25          | 54.35       |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)     | **47.70** | 72.33           | **76.87**     | 24.80     | **79.00** | 38.47     | **38.76**      | **67.96**      | **55.73**   |

See the technical report for more results and comparison.

## Evaluation

### Setup

Install the following dependencies:

```bash

# install public lm-eval-harness

harness_repo="public-lm-eval-harness"
git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo}
cd ${harness_repo}
# use main branch on 03-15-2024, SHA is dc90fec
git checkout dc90fec
pip install -e .
cd ..

# 66d6242 is the main branch on 2024-04-01 
pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242
pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0

```

### Evaluate OpenELM

```bash

# OpenELM-270M
hf_model=OpenELM-270M

# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMa tokenizer which requires add_bos_token to be True
add_bos_token=True
batch_size=1

mkdir lm_eval_output

shot=0
task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2
lm_eval --model hf \
        --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \
        --tasks ${task} \
        --device cuda:0 \
        --num_fewshot ${shot} \
        --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
        --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log

shot=5
task=mmlu,winogrande
lm_eval --model hf \
        --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \
        --tasks ${task} \
        --device cuda:0 \
        --num_fewshot ${shot} \
        --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
        --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log

shot=25
task=arc_challenge,crows_pairs_english
lm_eval --model hf \
        --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \
        --tasks ${task} \
        --device cuda:0 \
        --num_fewshot ${shot} \
        --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
        --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log

shot=10
task=hellaswag
lm_eval --model hf \
        --model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \
        --tasks ${task} \
        --device cuda:0 \
        --num_fewshot ${shot} \
        --output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
        --batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log

```


## Bias, Risks, and Limitations

Our OpenELM models are not trained with any safety guarantees, the model outputs can be potentially inaccurate, harmful or contain biased information. produce inaccurate, biased or other objectionable responses to user prompts. Therefore, users and developers should conduct extensive safety testing and filtering suited to their specific needs.