Update README.md

README.md

@@ -19,16 +19,16 @@ The core models released in this batch include the following:
 
 | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
 |------|--------|---------|-------------|-----------------|----------------|
-| [OLMo2-7B July 2024](https://huggingface.co/allenai/OLMo-7B-0724-hf) | 4 Trillion   | 32     | 4096        | 32              |  4096  |
-| [OLMo2- 13B July 2024](https://huggingface.co/allenai/OLMo-1B-0724-hf) | 5 Trillion   | 40     | 5120        | 42              |  4096  |
+| [OLMo2-7B](https://huggingface.co/allenai/OLMo-1124-7B) | 4 Trillion   | 32     | 4096        | 32              |  4096  |
+| [OLMo2- 13B](https://huggingface.co/allenai/OLMo2-1124-13B) | 5 Trillion   | 40     | 5120        | 42              |  4096  |
 
 ## Inference
 
 You can use OLMo with the standard HuggingFace transformers library:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo2-13B-1124")
-tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo2-13B-1124")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-1124-13B")
 message = ["Language modeling is "]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
@@ -41,7 +41,7 @@ print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
 
 For faster performance, you can quantize the model using the following method:
 ```python
-AutoModelForCausalLM.from_pretrained("allenai/OLMo2-13B-1124", 
+AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B", 
     torch_dtype=torch.float16, 
     load_in_8bit=True)  # Requires bitsandbytes package
 ```
@@ -55,13 +55,13 @@ 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/OLMo2-13B-1124", revision="step102500-tokens860B")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B", revision="step102500-tokens860B")
 ```
 
 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/OLMo2-13B-1124")
+out = list_repo_refs("allenai/OLMo-2-1124-13B")
 branches = [b.name for b in out.branches]
 ```