create app.py

app.py

@@ -0,0 +1,145 @@
+
+from unsloth import FastLanguageModel
+import torch
+import pandas as pd
+from datasets import Dataset
+import numpy as np
+from sklearn.model_selection import train_test_split
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from unsloth import is_bfloat16_supported
+
+max_seq_length = 4096 # 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.
+
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/tinyllama-bnb-4bit", # "unsloth/tinyllama" for 16bit loading
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 32,
+    lora_dropout = 0, # Currently only supports dropout = 0
+    bias = "none",    # Currently only supports bias = "none"
+    use_gradient_checkpointing = False, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+alpaca_prompt = """Below is an instruction that describes a task, paired with an output that provides correct output for that task. Write a response that produces correct solution to the problem
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token
+def formatting_prompts_func(examples):
+    instructions = "The problem has the following answer. Understand step-by-step how it is solved to produce the correct solution and then produce the correct solution"
+    inputs       = examples["Riddle"]
+    outputs      = examples["Answer"]
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        # Must add EOS_TOKEN, otherwise your generation will go on forever!
+        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+
+
+
+df = pd.read_csv('math_riddles.csv')
+train, test = train_test_split(df, test_size=0.2, random_state=42)
+train_ds = Dataset.from_pandas(train)
+test_ds = Dataset.from_pandas(test)
+
+tokenized_train = train_ds.map(formatting_prompts_func, batched=True,
+                               remove_columns=['Riddle', 'Answer', '__index_level_0__']) # Removing features
+
+tokenized_test = test_ds.map(formatting_prompts_func, batched=True,
+                               remove_columns=['Riddle', 'Answer']) # Removing features
+
+
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = tokenized_train,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 24,
+    packing = True, # Packs short sequences together to save time!
+    args = TrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 1,
+        warmup_ratio = 0.1,
+        num_train_epochs = 3,
+        learning_rate = 2e-5,
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.1,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = "outputs",
+        report_to = "none", # Use this for WandB etc
+    ),
+)
+
+trainer_stats = trainer.train()
+
+# Define inference function
+def inference(instruction, user_input):
+    prompt = alpaca_prompt.format(
+        instruction,
+        user_input,
+        ""  # Leave output blank for generation
+    )
+
+    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=64,
+        use_cache=True
+    )
+
+    # Fix: Define result before printing it
+    result = tokenizer.batch_decode(outputs)[0]
+    print(result)  # Now you can print it
+
+    # Extract just the generated response (after the prompt)
+    response_prefix = "### Response:"
+    if response_prefix in result:
+        result = result.split(response_prefix)[1].strip()
+
+    return result
+
+# Create Gradio interface
+import gradio as gr
+demo = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.Textbox(label="Instruction", value="Solve the problem"),
+        gr.Textbox(label="Input", value="There is a three digit number.The second digit is four times as big as the third digit, while the first digit is three less than the second digit.What is the number?")
+    ],
+    outputs="text",
+    title="Language Model Interface",
+    description="Enter an instruction and input to generate a response from the model."
+)
+
+
+demo.launch(share=True)