allenai
/

OLMo-7B-0724-hf

@@ -25,7 +25,8 @@ The core models released in this batch are the following:
 [Coming soon] We are releasing many checkpoints for these models, for every 1000 training steps.
-The naming convention is `stepXXX-tokensYYYB`.
 To load a specific model revision with HuggingFace, simply add the argument `revision`:
 ```bash
@@ -114,36 +115,36 @@ For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo?
 Core model results for the new and original 7B model are found below.
-| Task              | Llama-7b | Llama2-7b | Falcon-7b | Mpt-7b | OLMo-7B | Llama2-13b | **OLMo 1.7-7B** |
-|-------------------|----------|-----------|-----------|--------|---------|------------|-------------|
-| arc_c             | 44.5     | 48.5      | 47.5      | 46.5   | 48.5    | 52.8       | 42.5        |
-| arc_e             | 67.9     | 69.5      | 70.4      | 70.5   | 65.4    | 73.7       | 67.2        |
-| boolq             | 75.4     | 80.2      | 74.6      | 74.2   | 73.4    | 82.2       | 83.7        |
-| copa              | 91.0     | 86.0      | 86.0      | 85.0   | 90.0    | 90.0       | 86.0        |
-| hellaswag         | 76.2     | 76.8      | 75.9      | 77.6   | 76.4    | 78.6       | 75.5        |
-| openbookqa        | 51.2     | 48.4      | 53.0      | 48.6   | 50.4    | 51.8       | 50.0        |
-| piqa              | 77.2     | 76.7      | 78.5      | 77.3   | 78.4    | 79.0       | 77.5        |
-| sciq              | 93.9     | 94.5      | 93.9      | 93.7   | 93.8    | 95.5       | 96.7        |
-| winogrande        | 70.5     | 69.4      | 68.9      | 69.9   | 67.9    | 73.5       | 69.8        |
-| truthfulQA (MC2)  | 33.9     | 38.5      | 34.0      | 33.0   | 36.0    | 36.8       | 35.8        |
-| MMLU (5 shot MC)  | 31.5     | 45.0      | 24.0      | 30.8   | 28.3    | 55.5       | 52.0        |
-| GSM8k             | 10.0     | 12.0      | 4.0       | 4.5    | 8.5     | 25.0       | 29.0        |
-| Full average      | 60.3     | 62.1      | 59.2      | 59.3   | 59.8    | 66.2       | 63.8        |
 And for the 1B model:
-| task       | random | [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* | [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) | [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) | **OLMo 1B** (ours) |
-| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ----------------- | --------- | -------------------------------------- | ------- |
-| arc_challenge | 25     | 43.81             | 33.11     | 34.78                                  | 34.45   |
-| arc_easy      | 25     | 63.68             | 50.18     | 53.16                                  | 58.07   |
-| boolq         | 50     | 76.6              | 61.8      | 64.6                                   | 60.7    |
-| copa          | 50     | 84                | 72        | 78                                     | 79      |
-| hellaswag     | 25     | 68.2              | 44.7      | 58.7                                   | 62.5    |
-| openbookqa    | 25     | 45.8              | 37.8      | 43.6                                   | 46.4    |
-| piqa          | 50     | 74                | 69.1      | 71.1                                   | 73.7    |
-| sciq          | 25     | 94.7              | 86        | 90.5                                   | 88.1    |
-| winogrande    | 50     | 64.9              | 53.3      | 58.9                                   | 58.9    |
-| Average       | 36.11  | 68.41             | 56.44     | 61.48                                  | 62.42   |
 \*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
@@ -156,8 +157,8 @@ During the annealing phase we use a higher quality subset of Dolma with a linear
 ### Staged training / annealing
-In contrast to OLMo 1.0, we trained OLMo 1.7 with a two-stage curriculum:
-* In the first stage, we trained the model from scratch on the Dolma 1.7 dataset. We set a cosine learning rate schedule with a warmup of 2500 steps, a peak learning rate of 3e-4, and a cosine decay to 3e-5 after 3T tokens. We cut off this stage after 2T tokens, when the learning rate is still high.
 * At this point we switch to the second stage, in which we train on a higher-quality subset of Dolma 1.7 (see below) for another 50B tokens, while linearly decaying the learning rate to 0. Our high-quality subset includes (1) using all available Wikipedia, OpenWebMath and Flan data, (2) removing Dolma CC, CC News, and Megawika, and (3) rebalancing remaining sources to achieve approximately equal proportions of each. See exact token counts and relative proportions of this second stage mix below.
 Both stages contribute equally to the final performance of the OLMo model. After the first stage, OLMo 1.7 already outperforms OLMo 1.0. The second stage consistently adds 2 to 3 points of performance on top.
@@ -168,7 +169,7 @@ OLMo 7B architecture with peer models for comparison.
 |                        | **OLMo 7B**   | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | PaLM 8B |
 |------------------------|-------------------|---------------------|--------------------|--------------------|------------------|
-| d_model     | 4096              | 4096                | 4096               | 4544               | 4096             |
 | num heads              | 32                | 32                  | 32                 | 71                 | 16               |
 | num layers             | 32                | 32                  | 32                 | 32                 | 32               |
 | MLP ratio              | ~8/3         | ~8/3           | ~8/3          | 4                  | 4                |

 [Coming soon] We are releasing many checkpoints for these models, for every 1000 training steps.
+The naming convention is `stepXXX-tokensYYYB`. These checkpoints are already available at [OLMo 7B April 2024](https://huggingface.co/allenai/OLMo-1.7-7B-hf)
+and will be copied here soon.
 To load a specific model revision with HuggingFace, simply add the argument `revision`:
 ```bash
 Core model results for the new and original 7B model are found below.
+| Task              | Llama-7b | Llama2-7b | Falcon-7b | Mpt-7b | OLMo-7B | Llama2-13b | OLMo 7B April 2024 | **OLMo 7B July 2024** |
+|-------------------|----------|-----------|-----------|--------|---------|------------|--------------------|-----------------------|
+| arc_c             | 44.5     | 48.5      | 47.5      | 46.5   | 48.5    | 52.8       | 42.5               | 43.8                  |
+| arc_e             | 67.9     | 69.5      | 70.4      | 70.5   | 65.4    | 73.7       | 67.2               | 68.8                  |
+| boolq             | 75.4     | 80.2      | 74.6      | 74.2   | 73.4    | 82.2       | 83.7               | 78.9                  |
+| copa              | 91.0     | 86.0      | 86.0      | 85.0   | 90.0    | 90.0       | 86.0               | 84.0                  |
+| hellaswag         | 76.2     | 76.8      | 75.9      | 77.6   | 76.4    | 78.6       | 75.5               | 77.4                  |
+| openbookqa        | 51.2     | 48.4      | 53.0      | 48.6   | 50.4    | 51.8       | 50.0               | 48.2                  |
+| piqa              | 77.2     | 76.7      | 78.5      | 77.3   | 78.4    | 79.0       | 77.5               | 78.2                  |
+| sciq              | 93.9     | 94.5      | 93.9      | 93.7   | 93.8    | 95.5       | 96.7               | 97.0                  |
+| winogrande        | 70.5     | 69.4      | 68.9      | 69.9   | 67.9    | 73.5       | 69.8               | 68.8                  |
+| truthfulQA (MC2)  | 33.9     | 38.5      | 34.0      | 33.0   | 36.0    | 36.8       | 35.8               | 36.5                  |
+| MMLU (5 shot MC)  | 31.5     | 45.0      | 24.0      | 30.8   | 28.3    | 55.5       | 52.0               | 53.4                  |
+| GSM8k             | 10.0     | 12.0      | 4.0       | 4.5    | 8.5     | 25.0       | 29.0               | 35.0                  |
+| Full average      | 60.3     | 62.1      | 59.2      | 59.3   | 59.8    | 66.2       | 63.8               | 64.2                  |
 And for the 1B model:
+| task       | random | [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* | [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) | [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) | [OLMo 1B](https://huggingface.co/allenai/OLMo-1B-hf) | **OLMo 1B July 2024** |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ----------------- | --------- | -------------------------------------- | ------- | ------ |
+| arc_challenge | 25     | 43.81             | 33.11     | 34.78                                  | 34.45   | 36.5 |
+| arc_easy      | 25     | 63.68             | 50.18     | 53.16                                  | 58.07   | 55.3 |
+| boolq         | 50     | 76.6              | 61.8      | 64.6                                   | 60.7    | 67.5 |
+| copa          | 50     | 84                | 72        | 78                                     | 79      | 83.0 |
+| hellaswag     | 25     | 68.2              | 44.7      | 58.7                                   | 62.5    | 66.9 |
+| openbookqa    | 25     | 45.8              | 37.8      | 43.6                                   | 46.4    | 46.4 |
+| piqa          | 50     | 74                | 69.1      | 71.1                                   | 73.7    | 74.9 |
+| sciq          | 25     | 94.7              | 86        | 90.5                                   | 88.1    | 93.4 |
+| winogrande    | 50     | 64.9              | 53.3      | 58.9                                   | 58.9    | 61.4 |
+| Average       | 36.11  | 68.41             | 56.44     | 61.48                                  | 62.42   | 65.0 |
 \*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
 ### Staged training / annealing
+In contrast to OLMo 1.0, we trained OLMo 7B July with a two-stage curriculum:
+* In the first stage, we trained the model from scratch on the Dolma 1.7 dataset. We set a cosine learning rate schedule with a warmup of 2500 steps, a peak learning rate of 3e-4, and a cosine decay to 3e-5 after 3T tokens. We cut off this stage after 2.7T tokens, when the learning rate is still somewhat high.
 * At this point we switch to the second stage, in which we train on a higher-quality subset of Dolma 1.7 (see below) for another 50B tokens, while linearly decaying the learning rate to 0. Our high-quality subset includes (1) using all available Wikipedia, OpenWebMath and Flan data, (2) removing Dolma CC, CC News, and Megawika, and (3) rebalancing remaining sources to achieve approximately equal proportions of each. See exact token counts and relative proportions of this second stage mix below.
 Both stages contribute equally to the final performance of the OLMo model. After the first stage, OLMo 1.7 already outperforms OLMo 1.0. The second stage consistently adds 2 to 3 points of performance on top.
 |                        | **OLMo 7B**   | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | PaLM 8B |
 |------------------------|-------------------|---------------------|--------------------|--------------------|------------------|
+| d_model                | 4096              | 4096                | 4096               | 4544               | 4096             |
 | num heads              | 32                | 32                  | 32                 | 71                 | 16               |
 | num layers             | 32                | 32                  | 32                 | 32                 | 32               |
 | MLP ratio              | ~8/3         | ~8/3           | ~8/3          | 4                  | 4                |