Add pipeline tag

This PR adds the `pipeline_tag: text-generation` to the model card's metadata. This ensures that the model can be properly categorized and discovered on the Hugging Face Hub, appearing under searches for text generation models.

README.md

@@ -1,6 +1,5 @@
 ---
 base_model: google/gemma-2-9b
-license: cc-by-nc-sa-4.0
 language:
 - de
 - nl
@@ -25,6 +24,8 @@ language:
 - ro
 - fi
 library_name: transformers
+license: cc-by-nc-sa-4.0
+pipeline_tag: text-generation
 ---
 
 ![Tower Plus Pareto](./Tower-plus-pareto.png)
@@ -71,7 +72,9 @@ sampling_params = SamplingParams(
   max_tokens=8192,
 )
 llm = LLM(model="Unbabel/Tower-Plus-9B", tensor_parallel_size=1)
-messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
+messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):
+English: Hello world!
+Portuguese (Portugal): "}]
 outputs = llm.chat(messages, sampling_params)
 # Make sure your prompt_token_ids look like this
 print (outputs[0].outputs[0].text)
@@ -89,7 +92,9 @@ from transformers import pipeline
 
 pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-9B", device_map="auto")
 # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
+messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):
+English: Hello world!
+Portuguese (Portugal): "}]
 input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
 outputs = pipe(messages, max_new_tokens=256, do_sample=False)
 print(outputs[0]["generated_text"])