fixes to model card

README.md

@@ -23,23 +23,23 @@ We release all code, checkpoints, logs, and details involved in training these m
 The core models released in this batch are the following: 
 | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
 |------|--------|---------|-------------|-----------------|----------------|
-| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B)   | 3 Trillion |16     | 2048        | 16              | 2048  |
-| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) | 2.5 Trillion   | 32     | 4096        | 32              |  2048  |
-| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T) | 2 Trillion   | 32     | 4096        | 32              |  2048  |
-| [OLMo 1.7-7B](https://huggingface.co/allenai/OLMo-1.7-7B) | 2.05 Trillion   | 32     | 4096        | 32              |  4096  |
+| [OLMo 1B July 2024](https://huggingface.co/allenai/OLMo-1B-0724-hf) | 3.05 Trillion   | 16     | 2048        | 16              |  4096  |
+| [OLMo 7B July 2024](https://huggingface.co/allenai/OLMo-7B-0724-hf) | 2.75 Trillion   | 32     | 4096        | 32              |  4096  |
 
-*Note: OLMo 1.7-1B also includes QKV clipping.*
+
+[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
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-1B-hf", revision="step1000-tokens2B")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf", revision="step1000-tokens4B")
 ```
 
 All revisions/branches are listed in the file `revisions.txt`. 
 Or, you can access all the revisions for the models via the following code snippet:
 ```python
 from huggingface_hub import list_repo_refs
-out = list_repo_refs("allenai/OLMo-1.7-1B-hf")
+out = list_repo_refs("allenai/OLMo-1B-0724-hf")
 branches = [b.name for b in out.branches]
 ```
 
@@ -62,7 +62,7 @@ branches = [b.name for b in out.branches]
     - Evaluation code: https://github.com/allenai/OLMo-Eval
     - Further fine-tuning code: https://github.com/allenai/open-instruct
 - **Paper:** [Link](https://arxiv.org/abs/2402.00838)
-<!-- - **W&B Logs:** [pretraining](https://wandb.ai/ai2-llm/OLMo-7B/groups/OLMo-1.7-7B), [annealing](https://wandb.ai/ai2-llm/OLMo-7B/groups/OLMo-1.7-7B-anneal) -->
+
 
 ## Uses
 
@@ -71,8 +71,8 @@ branches = [b.name for b in out.branches]
 Install Transformers. Then proceed as usual with HuggingFace:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-1B-hf")
-tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1.7-1B-hf")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-0724-hf")
 message = ["Language modeling is "]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
@@ -85,12 +85,12 @@ print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
 Alternatively, with the pipeline abstraction:
 ```python
 from transformers import pipeline
-olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1.7-1B-hf")
+olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1B-0724-hf")
 print(olmo_pipe("Language modeling is "))
 >> 'Language modeling is a branch of natural language processing that aims to...'
 ```
 
-Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-1B-hf", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).
+Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).
 The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as `inputs.input_ids.to('cuda')` to avoid potential issues.
 
 ### Fine-tuning