update

flexflow · Sep 25, 2024 · f8c90e6 · f8c90e6
1 parent a2d2ac0
commit f8c90e6
Showing 1 changed file with 16 additions and 2 deletions.
diff --git a/inference/python/streamlit/app.py b/inference/python/streamlit/app.py
@@ -103,15 +103,17 @@ def generate_llama2_response(prompt_input):
         if 'hf_token' in st.session_state.keys():
             st.success('HF token already provided!', icon='✅')
             hf_token = st.session_state.hf_token
-            print(hf_token)
         else:
             hf_token = st.text_input('Enter your Hugging Face token:', type='password')
             if not (hf_token.startswith('hf_') and len(hf_token)==37):
-                st.warning('Please enter valid credentials!', icon='⚠️')
+                st.warning('please enter a valid token', icon='⚠️')
             else:
                 st.success('Proceed to finetuning your model!', icon='👉')
                 st.session_state.hf_token = hf_token
 
+        # PEFT model name
+        peft_model_name = st.text_input("Enter the PEFT model name:", help="The name of the PEFT model should start with the username associated with the provided HF token, followed by '/'ß. E.g. 'username/peft-base-uncased'")
+
         # Dataset selection
         dataset_option = st.radio("Choose dataset source:", ["Upload JSON", "Hugging Face Dataset"])
 
@@ -123,6 +125,18 @@ def generate_llama2_response(prompt_input):
         else:
             dataset_name = st.text_input("Enter Hugging Face dataset name:")
 
+        # Finetuning parameters
+        st.subheader("Finetuning parameters")
+        lora_rank = st.number_input("LoRA rank", min_value=2, max_value=64, value=16, step=2)
+        lora_alpha = st.number_input("LoRA alpha", min_value=2, max_value=64, value=16, step=2)
+        target_modules = st.multiselect("Target modules", ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"], default=["down_proj"])
+        learning_rate = st.number_input("Learning rate", min_value=1e-6, max_value=1e-3, value=1e-5, step=1e-6)
+        optimizer_type = st.selectbox("Optimizer type", ["SGD", "Adam", "AdamW", "Adagrad", "Adadelta", "Adamax", "RMSprop"])
+        momentum = st.number_input("Momentum", min_value=0.0, max_value=1.0, value=0.0, step=0.01)
+        weight_decay = st.number_input("Weight decay", min_value=0.0, max_value=1.0, value=0.0, step=0.01)
+        nesterov = st.checkbox("Nesterov")
+        max_steps = st.number_input("Max steps", min_value=1000, max_value=100000, value=10000, step=1000)
+
         # Start finetuning button
         if st.button("Start Finetuning"):
             if not hf_token: