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.gitattributes

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README.md

@@ -1,14 +1,6 @@
 ---
 library_name: transformers
 tags: []
-widget:
-  - text: |
-      <start_of_turn>user
-      How does the brain work?<end_of_turn>
-      <start_of_turn>model
-inference:
-  parameters:
-    max_new_tokens: 200
 extra_gated_heading: "Access Gemma on Hugging Face"
 extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
 extra_gated_button_content: "Acknowledge license"
@@ -27,7 +19,7 @@ This model card corresponds to the 7B instruct version of the Gemma model. You c
 
 * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
 * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
-* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-it-gg-hf)
+* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
 
 **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
 
@@ -73,9 +65,9 @@ tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
 model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it")
 
 input_text = "Write me a poem about Machine Learning."
-input_ids = tokenizer(input_text, return_tensors="pt")
+input_ids = tokenizer(**input_text, return_tensors="pt")
 
-outputs = model.generate(**input_ids)
+outputs = model.generate(input_ids)
 print(tokenizer.decode(outputs[0]))
 ```
 
@@ -309,7 +301,7 @@ several advantages in this domain:
 
 ### Software
 
-Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
+Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
 
 JAX allows researchers to take advantage of the latest generation of hardware,
 including TPUs, for faster and more efficient training of large models.

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