Fix pipeline_tag 🤗

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-pipeline_tag: image-text-to-text
+pipeline_tag: text-generation
 library_name: transformers
 ---
 
@@ -43,27 +43,33 @@ The following provides demo code illustrating how to generate text using JanusCo
 > Please use transformers >= 4.55.0 to ensure the model works normally.
 
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 model_name = "internlm/JanusCoder-14B"
+
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", dtype="auto",
+).eval()
 
 messages = [
-    {
-        "role": "user",
-        "content": [
-            {"type": "text", "text": "Create a line plot that illustrates function y=x."},
-        ],
-    }
+    {"role": "user", "content": "Create a line plot that illustrates function y=x."}
 ]
 
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device)
+
+with torch.inference_mode():
+    generate_ids = model.generate(**inputs, max_new_tokens=200)
+    decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)
 
-generate_ids = model.generate(**inputs, max_new_tokens=32768)
-decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
-print(decoded_output)
+print(decoded_output[0])
 ```
 
 ## Citation