Create app.py

app.py

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+from unsloth import FastLanguageModel
+import torch
+import gradio as gr
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
+fourbit_models = [
+    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
+    "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
+    "unsloth/llama-3-8b-bnb-4bit",           # Llama-3 15 trillion tokens model 2x faster!
+    "unsloth/llama-3-8b-Instruct-bnb-4bit",
+    "unsloth/llama-3-70b-bnb-4bit",
+    "unsloth/Phi-3-mini-4k-instruct",        # Phi-3 2x faster!
+    "unsloth/Phi-3-medium-4k-instruct",
+    "unsloth/mistral-7b-bnb-4bit",
+    "unsloth/gemma-7b-bnb-4bit",             # Gemma 2.2x faster!
+] # More models at https://huggingface.co/unsloth
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "danishmuhammad/ccat_2025_llama3.1_8B",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+FastLanguageModel.for_inference(model)
+
+alpaca_prompt = """Below is an input that describes a question, answer the following question as clearly as possible. If additional context is needed, provide it briefly.
+
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(layout="bubble")
+    user_input = gr.Textbox()
+    clear = gr.ClearButton([user_input, chatbot])
+
+    def answers_chat(user_input,history):
+      history = history or []
+      formatted_input = alpaca_prompt.format(user_input, "")
+      inputs = tokenizer([formatted_input], return_tensors="pt").to("cuda")
+
+        # Generate response with adjusted parameters
+      outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,  # Increase to allow for longer responses
+            temperature=0.4,  # Add temperature to introduce variation
+            repetition_penalty=1.2,  # Penalize repeating tokens
+            no_repeat_ngram_size=3,  # Avoid repeating sequences of 3 tokens
+            use_cache=True,
+            eos_token_id=tokenizer.eos_token_id
+        )
+      response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+
+      formatted_response = response[len(formatted_input):].strip()
+
+      history.append((user_input,formatted_response))
+
+      return "",history
+
+
+
+
+    user_input.submit(answers_chat, [user_input, chatbot], [user_input, chatbot])
+
+
+demo.launch()