another test

app.py

@@ -8,16 +8,17 @@ import gradio as gr
 from src.client.remote_model import DistributedBloomForCausalLM
 
 
-MODEL_NAME = "bigscience/test-bloomd-6b3" # select model you like
-INITIAL_PEERS = ["/ip4/193.106.95.184/tcp/31000/p2p/QmSg7izCDtowVTACbUmWvEiQZNY4wgCQ9T9Doo66K59X6q"]
+# MODEL_NAME = "bigscience/test-bloomd-6b3" # select model you like
+# INITIAL_PEERS = ["/ip4/193.106.95.184/tcp/31000/p2p/QmSg7izCDtowVTACbUmWvEiQZNY4wgCQ9T9Doo66K59X6q"]
 
-tokenizer = transformers.BloomTokenizerFast.from_pretrained("bigscience/test-bloomd-6b3")
-model = DistributedBloomForCausalLM.from_pretrained("bigscience/test-bloomd-6b3", initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32)
+# tokenizer = transformers.BloomTokenizerFast.from_pretrained("bigscience/test-bloomd-6b3")
+# model = DistributedBloomForCausalLM.from_pretrained("bigscience/test-bloomd-6b3", initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32)
 
 def inference(text, seq_length=1):
-    input_ids = tokenizer([text], return_tensors="pt").input_ids
-    output = model.generate(input_ids, max_new_tokens=seq_length)
-    return tokenizer.batch_decode(output)[0]
+    # input_ids = tokenizer([text], return_tensors="pt").input_ids
+    # output = model.generate(input_ids, max_new_tokens=seq_length)
+    # return tokenizer.batch_decode(output)[0]
+    return text
  
 iface = gr.Interface(
 fn=inference,