Integrating the DRL and LLM

app.py

@@ -10,6 +10,30 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 
+model = "tiiuae/falcon-7b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+sequences = pipeline(
+   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
+    max_length=200,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
+for seq in sequences:
+    st.write(f"Result: {seq['generated_text']}")
+
+
 def perform_training(jammer_type, channel_switching_cost):
     agent = train(jammer_type, channel_switching_cost)
     return agent
@@ -19,13 +43,13 @@ def perform_testing(agent, jammer_type, channel_switching_cost):
     test(agent, jammer_type, channel_switching_cost)
 
 
-model_name = "tiiuae/falcon-7b-instruct"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=100,
-                                 temperature=0.7)
+# model_name = "tiiuae/falcon-7b-instruct"
+# model = AutoModelForCausalLM.from_pretrained(model_name)
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=100,
+#                                  temperature=0.7)
 
-st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
+# st.title("Beyond the Anti-Jam: Integration of DRL with LLM")
 
 st.sidebar.header("Make Your Environment Configuration")
 mode = st.sidebar.radio("Choose Mode", ["Auto", "Manual"])
@@ -46,6 +70,6 @@ start_button = st.sidebar.button('Start')
 if start_button:
     agent = perform_training(jammer_type, channel_switching_cost)
     st.subheader("Generating Insights of the DRL-Training")
-    text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.")
-    st.write(text)
+    # text = pipeline("Discuss this topic: Integrating LLMs to DRL-based anti-jamming.")
+    # st.write(text)
     test(agent, jammer_type, channel_switching_cost)