Update space

app.py

@@ -1,161 +1,228 @@
+# # import gradio as gr
+# # from huggingface_hub import InferenceClient
+
+# # """
+# # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# # """
+# # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+# # def respond(
+# #     message,
+# #     history: list[tuple[str, str]],
+# #     system_message,
+# #     max_tokens,
+# #     temperature,
+# #     top_p,
+# # ):
+# #     messages = [{"role": "system", "content": system_message}]
+
+# #     for val in history:
+# #         if val[0]:
+# #             messages.append({"role": "user", "content": val[0]})
+# #         if val[1]:
+# #             messages.append({"role": "assistant", "content": val[1]})
+
+# #     messages.append({"role": "user", "content": message})
+
+# #     response = ""
+
+# #     for message in client.chat_completion(
+# #         messages,
+# #         max_tokens=max_tokens,
+# #         stream=True,
+# #         temperature=temperature,
+# #         top_p=top_p,
+# #     ):
+# #         token = message.choices[0].delta.content
+
+# #         response += token
+# #         yield response
+
+
+# # """
+# # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# # """
+# # demo = gr.ChatInterface(
+# #     respond,
+# #     additional_inputs=[
+# #         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+# #         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+# #         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+# #         gr.Slider(
+# #             minimum=0.1,
+# #             maximum=1.0,
+# #             value=0.95,
+# #             step=0.05,
+# #             label="Top-p (nucleus sampling)",
+# #         ),
+# #     ],
+# # )
+
+
+# # if __name__ == "__main__":
+# #     demo.launch()
+
+# import torch
 # import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# """
-# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-# """
-# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens,
-#     temperature,
-#     top_p,
-# ):
-#     messages = [{"role": "system", "content": system_message}]
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# import os
+
+# # Define model names
+# MODEL_1_PATH = "./adapter_model.safetensors"  # Local path inside Space
+# MODEL_2_NAME = "sarvamai/sarvam-1"  # The base model on Hugging Face Hub
+
+# # Load the tokenizer (same for both models)
+# TOKENIZER_NAME = "sarvamai/sarvam-1"
+# tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
+# def fix_checkpoint(model_path):
+#     """Fixes the model checkpoint by adjusting mismatched weight dimensions."""
+#     checkpoint_file = os.path.join(model_path, "pytorch_model.bin")
+#     fixed_checkpoint_file = os.path.join(model_path, "pytorch_model_fixed.bin")
+
+#     if not os.path.exists(checkpoint_file):
+#         raise FileNotFoundError(f"Checkpoint file not found at: {checkpoint_file}")
+
+#     print("Loading checkpoint for fixing...")
+#     checkpoint = torch.load(checkpoint_file, map_location="cpu")
+
+#     # Adjust weights (truncate the last token if mismatch)
+#     if "base_model.model.lm_head.base_layer.weight" in checkpoint:
+#         checkpoint["base_model.model.lm_head.base_layer.weight"] = checkpoint["base_model.model.lm_head.base_layer.weight"][:-1]
+
+#     if "base_model.model.lm_head.lora_B.default.weight" in checkpoint:
+#         checkpoint["base_model.model.lm_head.lora_B.default.weight"] = checkpoint["base_model.model.lm_head.lora_B.default.weight"][:-1]
+
+#     # Save the fixed checkpoint
+#     print("Saving fixed checkpoint...")
+#     torch.save(checkpoint, fixed_checkpoint_file)
+
+#     return fixed_checkpoint_file  # Return the new file path
+
+# # Function to load a model
+# def load_model(model_choice):
+#     if model_choice == "Hugging face dataset":
+#         model = AutoModelForCausalLM.from_pretrained("./", torch_dtype=torch.float16, device_map="auto")
+#         model.load_adapter(MODEL_1_PATH, "safe_tensors")  # Load safetensors adapter
+#     else:
+#         model = AutoModelForCausalLM.from_pretrained(MODEL_2_NAME)
+#     model.eval()
+#     return model
+
+# # Load default model on startup
+# current_model = load_model("Hugging face dataset")
+
+# # Chatbot response function
+# def respond(message, history, model_choice, max_tokens, temperature, top_p):
+#     global current_model
+    
+#     # Switch model if user selects a different one
+#     if (model_choice == "Hugging face dataset" and current_model is not None and current_model.config.name_or_path != MODEL_1_PATH) or \
+#        (model_choice == "Proprietary dataset1" and current_model is not None and current_model.config.name_or_path != MODEL_2_NAME):
+#         current_model = load_model(model_choice)
 
+#     # Convert chat history to format
+#     messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
 #     for val in history:
 #         if val[0]:
 #             messages.append({"role": "user", "content": val[0]})
 #         if val[1]:
 #             messages.append({"role": "assistant", "content": val[1]})
-
 #     messages.append({"role": "user", "content": message})
 
-#     response = ""
+#     # Tokenize and generate response
+#     inputs = tokenizer.apply_chat_template(messages, tokenize=False)
+#     input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
 
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
+#     output_tokens = current_model.generate(
+#         **input_tokens,
+#         max_new_tokens=max_tokens,
 #         temperature=temperature,
 #         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
+#         pad_token_id=tokenizer.pad_token_id,
+#         eos_token_id=tokenizer.eos_token_id,
+#     )
 
-#         response += token
-#         yield response
+#     response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+#     return response
 
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
+# # Define Gradio Chat Interface
 # demo = gr.ChatInterface(
-#     respond,
+#     fn=respond,
 #     additional_inputs=[
-#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
+#         gr.Dropdown(choices=["Hugging face dataset", "Proprietary dataset1"], value="Fine-Tuned Model", label="Select Model"),
+#         gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
+#         gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
 #     ],
 # )
 
-
 # if __name__ == "__main__":
 #     demo.launch()
 
 import torch
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Define model names
-MODEL_1_PATH = "./adapter_model.safetensors"  # Local path inside Space
-MODEL_2_NAME = "sarvamai/sarvam-1"  # The base model on Hugging Face Hub
+# Define model and tokenizer paths
+MODEL_1_PATH = "Priyanka6/fine-tuning-inference"
+TOKENIZER_NAME = "sarvam/sarvam-1"  # Keep this unchanged if tokenizer hasn't changed
 
-# Load the tokenizer (same for both models)
-TOKENIZER_NAME = "sarvamai/sarvam-1"
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
-def fix_checkpoint(model_path):
-    """Fixes the model checkpoint by adjusting mismatched weight dimensions."""
-    checkpoint_file = os.path.join(model_path, "pytorch_model.bin")
-    fixed_checkpoint_file = os.path.join(model_path, "pytorch_model_fixed.bin")
-
-    if not os.path.exists(checkpoint_file):
-        raise FileNotFoundError(f"Checkpoint file not found at: {checkpoint_file}")
-
-    print("Loading checkpoint for fixing...")
-    checkpoint = torch.load(checkpoint_file, map_location="cpu")
-
-    # Adjust weights (truncate the last token if mismatch)
-    if "base_model.model.lm_head.base_layer.weight" in checkpoint:
-        checkpoint["base_model.model.lm_head.base_layer.weight"] = checkpoint["base_model.model.lm_head.base_layer.weight"][:-1]
-
-    if "base_model.model.lm_head.lora_B.default.weight" in checkpoint:
-        checkpoint["base_model.model.lm_head.lora_B.default.weight"] = checkpoint["base_model.model.lm_head.lora_B.default.weight"][:-1]
-
-    # Save the fixed checkpoint
-    print("Saving fixed checkpoint...")
-    torch.save(checkpoint, fixed_checkpoint_file)
-
-    return fixed_checkpoint_file  # Return the new file path
-
-# Function to load a model
-def load_model(model_choice):
-    if model_choice == "Hugging face dataset":
-        model = AutoModelForCausalLM.from_pretrained("./", torch_dtype=torch.float16, device_map="auto")
-        model.load_adapter(MODEL_1_PATH, "safe_tensors")  # Load safetensors adapter
-    else:
-        model = AutoModelForCausalLM.from_pretrained(MODEL_2_NAME)
-    model.eval()
-    return model
-
-# Load default model on startup
-current_model = load_model("Hugging face dataset")
-
-# Chatbot response function
-def respond(message, history, model_choice, max_tokens, temperature, top_p):
-    global current_model
+def trim_adapter_weights(model_path):
+    """
+    Trims the last token from the adapter's lm_head.lora_B.default.weight 
+    if there is a mismatch with the base model.
+    """
+    adapter_file = os.path.join(model_path, "adapter_model.safetensors")
     
-    # Switch model if user selects a different one
-    if (model_choice == "Hugging face dataset" and current_model is not None and current_model.config.name_or_path != MODEL_1_PATH) or \
-       (model_choice == "Proprietary dataset1" and current_model is not None and current_model.config.name_or_path != MODEL_2_NAME):
-        current_model = load_model(model_choice)
-
-    # Convert chat history to format
-    messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-    messages.append({"role": "user", "content": message})
-
-    # Tokenize and generate response
-    inputs = tokenizer.apply_chat_template(messages, tokenize=False)
-    input_tokens = tokenizer(inputs, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-
-    output_tokens = current_model.generate(
-        **input_tokens,
-        max_new_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        pad_token_id=tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-
-    response = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-    return response
-
-# Define Gradio Chat Interface
-demo = gr.ChatInterface(
-    fn=respond,
-    additional_inputs=[
-        gr.Dropdown(choices=["Hugging face dataset", "Proprietary dataset1"], value="Fine-Tuned Model", label="Select Model"),
-        gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max Tokens"),
-        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
-    ],
+    if not os.path.exists(adapter_file):
+        raise FileNotFoundError(f"Adapter file not found: {adapter_file}")
+    
+    checkpoint = torch.load(adapter_file, map_location="cpu")
+
+    key_to_trim = "lm_head.lora_B.default.weight"
+    
+    if key_to_trim in checkpoint:
+        original_size = checkpoint[key_to_trim].shape[0]
+        expected_size = original_size - 1  # Removing last token
+        
+        print(f"Trimming {key_to_trim}: {original_size} -> {expected_size}")
+
+        checkpoint[key_to_trim] = checkpoint[key_to_trim][:-1]  # Trim the last row
+
+        # Save the modified adapter
+        trimmed_adapter_path = os.path.join(model_path, "adapter_model_trimmed.safetensors")
+        torch.save(checkpoint, trimmed_adapter_path)
+        return trimmed_adapter_path
+    
+    return adapter_file
+
+# Before loading the adapter, trim it if necessary
+trimmed_adapter_path = trim_adapter_weights(MODEL_1_PATH)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_1_PATH, torch_dtype=torch.float16, device_map="auto"
 )
 
+# Load the trimmed adapter
+model.load_adapter(trimmed_adapter_path, "safe_tensors")  
+
+# Chat function
+def chat(query):
+    inputs = tokenizer(query, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        output = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Test the chatbot
 if __name__ == "__main__":
-    demo.launch()
+    while True:
+        query = input("User: ")
+        if query.lower() in ["exit", "quit"]:
+            break
+        response = chat(query)
+        print(f"Bot: {response}")