Update space

app.py

@@ -1,141 +1,65 @@
-# # import gradio as gr
-# # from huggingface_hub import InferenceClient
+# import torch
+# import gradio as gr
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# # Load the model and tokenizer
+# MODEL_NAME = "sarvamai/sarvam-1"
+# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+# model.eval()
+
+# def respond(message, history, max_tokens, temperature, top_p):
+#     # 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 = 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.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"),
+#     ],
+#     title="Sarvam-1 Chat Interface",
+#     description="Chat with the Sarvam-1 language model"
+# )
 
-# # """
-# # 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()
+# if __name__ == "__main__":
+#     demo.launch()
 
 import torch
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import os
-from safetensors.torch import load_file, save_file
 
-# Define model names
-# MODEL_1_PATH = "./adapter_model.safetensors"  # Local path inside Space
-###
-MODEL_1_PATH = "Priyanka6/fine-tuning-inference"
-###
-MODEL_2_NAME = "sarvamai/sarvam-1"  # The base model on Hugging Face Hub
-# MODEL_3_NAME = 
-
-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. 
-    """
-    model_path = "./adapter_model.safetensors"
-    # if not os.path.exists(model_path):
-    #     raise FileNotFoundError(f"Adapter file not found: {model_path}")
-    
-    checkpoint = load_file(model_path)
-    print("Keys in checkpoint:", list(checkpoint.keys()))
-
-    key_to_trim = "lm_head.lora_B.default.weight"
-    
-    if key_to_trim in checkpoint:
-        print("Entered")
-        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")
-        save_file(checkpoint, trimmed_adapter_path)
-        return trimmed_adapter_path
-    print("didn't execute the if block!")
-    return model_path
-model_path=os.path.join(MODEL_1_PATH,"adapter_model.safetensors")
-trimmed_adapter_path = trim_adapter_weights(model_path)
-
-# Load the tokenizer (same for both models)
-TOKENIZER_NAME = "sarvamai/sarvam-1"
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
-
-# 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")
-        trimmed_adapter_path = os.path.join("Priyanka6/fine-tuning-inference", "adapter_model_trimmed.safetensors")
-        model.load_adapter(trimmed_adapter_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)
+# Load the model and tokenizer
+MODEL_NAME = "sarvamai/sarvam-1"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+model.eval()
 
+def respond(message, history, max_tokens, temperature, top_p):
     # Convert chat history to format
     messages = [{"role": "system", "content": "You are a friendly AI assistant."}]
     for val in history:
@@ -149,7 +73,7 @@ def respond(message, history, model_choice, max_tokens, temperature, top_p):
     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(
+    output_tokens = model.generate(
         **input_tokens,
         max_new_tokens=max_tokens,
         temperature=temperature,
@@ -165,22 +89,13 @@ def respond(message, history, model_choice, max_tokens, temperature, top_p):
 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"),
     ],
+    title="Sarvam-1 Chat Interface",
+    description="Chat with the Sarvam-1 language model"
 )
 
 if __name__ == "__main__":
-    demo.launch()
-
-
-# # Test the chatbot
-# if __name__ == "__main__":
-#     while True:
-#         query = input("User: ")
-#         if query.lower() in ["exit", "quit"]:
-#             break
-#         response = chat(query)
-#         print(f"Bot: {response}")
+    demo.launch()