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+# Auto detect text files and perform LF normalization
+* text=auto

README.md

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 ---
 title: Mistral Nemo 12b Unsloth 2x Faster Finetuning By Skk
-emoji: 🔥
-colorFrom: red
-colorTo: red
+emoji: 🏢
+colorFrom: purple
+colorTo: yellow
 sdk: gradio
 sdk_version: 4.39.0
 app_file: app.py

app.py

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+import gradio as gr
+from unsloth import FastLanguageModel
+import torch
+
+# Load the model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="skkjodhpur/Mistral-Nemo-12b-Unsloth-2x-faster-finetuning-by-skk",
+    max_seq_length=2048,
+    load_in_4bit=True
+)
+FastLanguageModel.for_inference(model)
+
+# Define the function to generate output
+def generate_response(instruction, input_text):
+    alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+    ### Instruction:
+    {}
+
+    ### Input:
+    {}
+
+    ### Response:
+    {}"""
+    
+    inputs = tokenizer(
+        [alpaca_prompt.format(instruction, input_text, "")],
+        return_tensors="pt"
+    ).to("cuda")
+
+    output = model.generate(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, max_new_tokens=64, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio interface
+with gr.Blocks(css=".gradio-container {background-color: #f0f4f8; padding: 20px; border-radius: 10px;} .input-text {background-color: #e0f7fa; border: 1px solid #00796b; border-radius: 5px;} .output-text {background-color: #fff3e0; border: 1px solid #e65100; border-radius: 5px;}") as demo:
+    gr.Markdown("<h1 style='text-align: center; color: #00796b;'>AI Response Generator</h1>")
+    
+    with gr.Row():
+        instruction_input = gr.Textbox(label="Instruction", placeholder="Enter instruction here...", lines=2, css="input-text")
+    
+    with gr.Row():
+        context_input = gr.Textbox(label="Input Context", placeholder="Enter input context here...", lines=4, css="input-text")
+    
+    with gr.Row():
+        submit_button = gr.Button("Generate Response", variant="primary")
+
+    output_text = gr.Textbox(label="Generated Response", interactive=False, lines=4, css="output-text")
+
+    submit_button.click(generate_response, inputs=[instruction_input, context_input], outputs=output_text)
+
+# Launch the interface
+demo.launch()

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+# Unsloth and its dependencies
+git+https://github.com/unslothai/unsloth.git#egg=unsloth[colab-new]
+xformers<0.0.27
+trl<0.9.0
+peft
+accelerate
+bitsandbytes
+gradio
+torch  # Specify the version if needed, e.g., torch==1.13.1
+transformers  # Specify the version if needed, e.g., transformers==4.21.1