HuggingFaceTB
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finemath-ablation-3plus-160B

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loubnabnl HF staff commited on 9 days ago

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Update README.md

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@@ -31,7 +31,7 @@ It is important to note that the primary intended use case of this model is to c
 # pip install -q transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = "HuggingFaceTB/finemath-ablation-finemath-infimath-3plus"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(model)
@@ -48,12 +48,12 @@ We are releasing intermediate checkpoints for this model at intervals of every 1
 You can load a specific model revision with `transformers` using the argument `revision`:
 ```python
-model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/finemath-ablation-finemath-infimath-3plus", revision="10B")
 ```
 You can access all the revisions for the models via the following code:
 ```python
 from huggingface_hub import list_repo_refs
-out = list_repo_refs("HuggingFaceTB/finemath-ablation-finemath-infimath-3plus")
 print([b.name for b in out.branches])
 ```

 # pip install -q transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model = "HuggingFaceTB/finemath-ablation-3plus-160B"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(model)
 You can load a specific model revision with `transformers` using the argument `revision`:
 ```python
+model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/finemath-ablation-3plus-160B", revision="10B")
 ```
 You can access all the revisions for the models via the following code:
 ```python
 from huggingface_hub import list_repo_refs
+out = list_repo_refs("HuggingFaceTB/finemath-ablation-3plus-160B")
 print([b.name for b in out.branches])
 ```