Update README.md

README.md

@@ -25,19 +25,19 @@ 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`. These checkpoints are already available at [OLMo 7B April 2024](https://huggingface.co/allenai/OLMo-1.7-7B-hf)
+The naming convention is `stepXXX-tokensYYYB`. These checkpoints are already available at [OLMo 7B April 2024](https://huggingface.co/allenai/OLMo-7B-0424-hf)
 and will be copied here soon.
 
 To load a specific model revision with HuggingFace, simply add the argument `revision`:
 ```bash
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0424-hf", revision="step1000-tokens4B")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-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-7B-0424-hf")
+out = list_repo_refs("allenai/OLMo-7B-0724-hf")
 branches = [b.name for b in out.branches]
 ```
 
@@ -73,8 +73,8 @@ Install Transformers [from source](https://huggingface.co/docs/transformers/en/i
 Now, proceed as usual with HuggingFace:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0424-hf")
-tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0424-hf")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0724-hf")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0724-hf")
 message = ["Language modeling is "]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
@@ -87,12 +87,12 @@ 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-7B-0424-hf")
+olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B-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-7B-0424-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-7B-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