Dates instead of 1.7

README.md

@@ -113,7 +113,7 @@ 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** |
+| Task              | Llama-7b | Llama2-7b | Falcon-7b | Mpt-7b | OLMo-7B | Llama2-13b | **OLMo 7B 0424** |
 |-------------------|----------|-----------|-----------|--------|---------|------------|-------------|
 | 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        |
@@ -131,7 +131,7 @@ Core model results for the new and original 7B model are found below.
 
 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 | **OLMo 1.7-1B** (ours) |
+| 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 | **OLMo 1B 0724** (ours) |
 | ------------- | ------ | ----------------- | --------- | -------------------------------------- | ------- | ---- |
 | arc_challenge | 25     | 43.8              | 33.1      | 34.8                                   | 34.5    | 36.5 |
 | arc_easy      | 25     | 63.7              | 50.2      | 53.2                                   | 58.1    | 55.3 |
@@ -155,10 +155,10 @@ 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 contrast to the first OLMo, we trained OLMo 7B 0424 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.
+Both stages contribute equally to the final performance of the OLMo model. After the first stage, OLMo 7B 0424 already outperforms the older OLMo. The second stage consistently adds 2 to 3 points of performance on top.
 
 
 ### Architecture