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--- |
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license: other |
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license_name: apple-sample-code-license |
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license_link: LICENSE |
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--- |
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# OpenELM |
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*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari* |
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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. |
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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. |
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See the list below for the details of each model: |
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- [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) |
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- [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) |
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- [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) |
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- [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) |
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- [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) |
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- [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) |
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- [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) |
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- [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) |
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```python |
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from transformers import AutoModelForCausalLM |
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openelm_270m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M", trust_remote_code=True) |
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openelm_450m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M", trust_remote_code=True) |
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openelm_1b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B", trust_remote_code=True) |
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openelm_3b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B", trust_remote_code=True) |
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openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True) |
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openelm_450m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M-Instruct", trust_remote_code=True) |
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openelm_1b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B-Instruct", trust_remote_code=True) |
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openelm_3b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B-Instruct", trust_remote_code=True) |
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``` |
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## Usage |
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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`. |
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You can try the model by running the following command: |
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``` |
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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 |
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``` |
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Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token. |
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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: |
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``` |
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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 |
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``` |
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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: |
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``` |
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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] |
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``` |
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## Main Results |
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### Zero-Shot |
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| **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** | |
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|-----------------------------------------------------------------------------|-----------|-----------|-----------|---------------|-----------|-----------|----------------|-------------| |
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| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 26.45 | 45.08 | **53.98** | 46.71 | 69.75 | **84.70** | **53.91** | 54.37 | |
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| [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** | |
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| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 27.56 | 48.06 | 55.78 | 53.97 | 72.31 | 87.20 | 58.01 | 57.56 | |
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| [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** | |
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| [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 | |
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| [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** | |
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| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 35.58 | 59.89 | 67.40 | 72.44 | 78.24 | **92.70** | 65.51 | 67.39 | |
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| [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** | |
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### LLM360 |
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| **Model Size** | **ARC-c** | **HellaSwag** | **MMLU** | **TruthfulQA** | **WinoGrande** | **Average** | |
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|-----------------------------------------------------------------------------|-----------|---------------|-----------|----------------|----------------|-------------| |
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| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | 47.15 | 25.72 | **39.24** | **53.83** | 38.72 | |
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| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | **51.58** | **26.70** | 38.72 | 53.20 | **40.54** | |
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| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | 53.86 | **26.01** | 40.18 | 57.22 | 41.50 | |
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| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | **59.31** | 25.41 | **40.48** | **58.33** | **43.41** | |
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| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | 65.71 | **27.05** | 36.98 | 63.22 | 45.93 | |
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| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | **71.83** | 25.65 | **45.95** | **64.72** | **49.94** | |
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| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | 73.28 | **26.76** | 34.98 | 67.25 | 48.90 | |
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| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | **76.87** | 24.80 | **38.76** | **67.96** | **51.22** | |
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### OpenLLM Leaderboard |
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| **Model Size** | **ARC-c** | **CrowS-Pairs** | **HellaSwag** | **MMLU** | **PIQA** | **RACE** | **TruthfulQA** | **WinoGrande** | **Average** | |
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|-----------------------------------------------------------------------------|-----------|-----------------|---------------|-----------|-----------|-----------|----------------|----------------|-------------| |
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| [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 | |
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| [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** | |
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| [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 | |
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| [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** | |
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| [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 | |
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| [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** | |
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| [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 | |
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| [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** | |
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See the technical report for more results and comparison. |
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## Evaluation |
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### Setup |
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Install the following dependencies: |
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```bash |
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# install public lm-eval-harness |
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harness_repo="public-lm-eval-harness" |
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git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo} |
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cd ${harness_repo} |
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# use main branch on 03-15-2024, SHA is dc90fec |
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git checkout dc90fec |
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pip install -e . |
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cd .. |
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# 66d6242 is the main branch on 2024-04-01 |
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pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242 |
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pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0 |
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``` |
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### Evaluate OpenELM |
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```bash |
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# OpenELM-270M |
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hf_model=OpenELM-270M |
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# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMa tokenizer which requires add_bos_token to be True |
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add_bos_token=True |
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batch_size=1 |
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mkdir lm_eval_output |
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shot=0 |
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task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2 |
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lm_eval --model hf \ |
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--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \ |
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--tasks ${task} \ |
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--device cuda:0 \ |
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--num_fewshot ${shot} \ |
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--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ |
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--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log |
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shot=5 |
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task=mmlu,winogrande |
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lm_eval --model hf \ |
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--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \ |
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--tasks ${task} \ |
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--device cuda:0 \ |
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--num_fewshot ${shot} \ |
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--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ |
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--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log |
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shot=25 |
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task=arc_challenge,crows_pairs_english |
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lm_eval --model hf \ |
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--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \ |
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--tasks ${task} \ |
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--device cuda:0 \ |
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--num_fewshot ${shot} \ |
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--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ |
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--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log |
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shot=10 |
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task=hellaswag |
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lm_eval --model hf \ |
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--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token} \ |
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--tasks ${task} \ |
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--device cuda:0 \ |
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--num_fewshot ${shot} \ |
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--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \ |
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--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log |
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``` |
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## Bias, Risks, and Limitations |
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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. |
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