add demo

app.py

@@ -0,0 +1,101 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from datetime import datetime
+
+model_id = "BSC-LT/salamandra-2b-instruct"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+description = """
+Salamandra-2b-instruct is a Transformer-based decoder-only language model that has been pre-trained on 7.8 trillion tokens of highly curated data. 
+The pre-training corpus contains text in 35 European languages and code. This instruction-tuned variant can be used as a general-purpose assistant.
+"""
+
+join_us = """
+## Join us:
+🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 
+[![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/qdfnvSPcqP) 
+On 🤗Huggingface: [MultiTransformer](https://huggingface.co/MultiTransformer) 
+On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/contribute)
+🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
+"""
+
+def generate_text(prompt, temperature, max_new_tokens, top_p, repetition_penalty):
+    date_string = datetime.today().strftime('%Y-%m-%d')
+    message = [{"role": "user", "content": prompt}]
+    
+    chat_prompt = tokenizer.apply_chat_template(
+        message,
+        tokenize=False,
+        add_generation_prompt=True,
+        date_string=date_string
+    )
+    
+    inputs = tokenizer.encode(chat_prompt, add_special_tokens=False, return_tensors="pt")
+    
+    outputs = model.generate(
+        input_ids=inputs.to(model.device),
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True
+    )
+    
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text.split("assistant\n")[-1].strip()
+
+def update_output(prompt, temperature, max_new_tokens, top_p, repetition_penalty):
+    return generate_text(prompt, temperature, max_new_tokens, top_p, repetition_penalty)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🙋🏻‍♂️ Welcome to Tonic's 📲🦎Salamandra-2b-instruct Demo")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown(description)
+        with gr.Column(scale=1):
+            gr.Markdown(join_us)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt = gr.Textbox(lines=5, label="🙋‍♂️ Input Prompt")
+            generate_button = gr.Button("Try 📲🦎Salamandra-2b-instruct")
+            
+            with gr.Accordion("🧪 Parameters", open=False):
+                temperature = gr.Slider(0.0, 1.0, value=0.7, label="🌡️ Temperature")
+                max_new_tokens = gr.Slider(1, 1000, value=200, step=1, label="🔢 Max New Tokens")
+                top_p = gr.Slider(0.0, 1.0, value=0.95, label="⚛️ Top P")
+                repetition_penalty = gr.Slider(1.0, 2.0, value=1.2, label="🔁 Repetition Penalty")
+        
+        with gr.Column(scale=1):
+            output = gr.Textbox(lines=10, label="📲🦎Salamandra")
+    
+    generate_button.click(
+        update_output,
+        inputs=[prompt, temperature, max_new_tokens, top_p, repetition_penalty],
+        outputs=output
+    )
+    
+    gr.Examples(
+        examples=[
+            ["What are the main advantages of living in a big city like Barcelona?"],
+            ["Explain the process of photosynthesis in simple terms."],
+            ["What are some effective strategies for learning a new language?"],
+            ["Describe the potential impacts of artificial intelligence on the job market in the next decade."],
+            ["What are the key differences between renewable and non-renewable energy sources?"]
+        ],
+        inputs=prompt,
+        outputs=prompt,
+        label="Example Prompts"
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch
+accelerate
+sentencepiece
+protobuf