Rename OLMo model from OLMo-7B to OLMo-1B (#2)

- Rename OLM model from OLMo-7B to OLMo-1B (381fbabdcbc874ac639d24aa18b151f237c63bd1)


Co-authored-by: Taufiq Dwi Purnomo <taufiqdp@users.noreply.huggingface.co>

README.md

@@ -93,8 +93,8 @@ Now, proceed as usual with HuggingFace:
 import hf_olmo
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
-olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B")
-tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B")
+olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B")
 message = ["Language modeling is "]
 inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
@@ -109,12 +109,12 @@ Alternatively, with the pipeline abstraction:
 import hf_olmo
 
 from transformers import pipeline
-olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B")
+olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1B")
 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", 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", 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.
 
 Note, you may see the following error if `ai2-olmo` is not installed correctly, which is caused by internal Python check naming. We'll update the code soon to make this error clearer.