Update app.py

README.md

@@ -4,15 +4,11 @@ emoji: 🫡
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.26.0
+sdk_version: 4.22.0
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
 short_description: Command-R is optimized for conversational interaction.
 ---
 
-# Changes to README.md 
-
-- 4/10/2024 update to rerank
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -6,7 +6,7 @@ import torch
 
 title = """
 # Welcome to 🌟Tonic's🫡Command-R
-🫡Command-R is a Large Language Model optimized for conversational interaction and long context tasks. It targets the “scalable” category of models that balance high performance with strong accuracy, enabling companies to move beyond proof of concept, and into production. 🫡Command-R boasts high precision on retrieval augmented generation (RAG) and tool use tasks, low latency and high throughput, a long 128k context, and strong capabilities across 10 key languages. You can build with this endpoint using🫡Command-R available here : [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01). You can also use 🫡Command-R by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/Command-R?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
+🫡Command-R is a Large Language Model optimized for conversational interaction and long context tasks. It targets the “scalable” category of models that balance high performance with strong accuracy, enabling companies to move beyond proof of concept, and into production. 🫡Command-R boasts high precision on retrieval augmented generation (RAG) and tool use tasks, low latency and high throughput, a long 128k context, and strong capabilities across 10 key languages. You can build with this endpoint using✨StarCoder available here : [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b). You can also use 🫡Command-R by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/Command-R?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
 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/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) Math 🔍 [introspector](https://huggingface.co/introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Torchon](https://github.com/Tonic-AI/Torchon)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
 """
 
@@ -20,34 +20,25 @@ model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_c
 def generate_response(user_input, max_new_tokens, temperature):
     messages = [{"role": "user", "content": user_input}]
     input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-    input_ids = input_ids.to(model.device)
+
+    # Generate tokens
     gen_tokens = model.generate(
-        input_ids = input_ids, 
-        max_new_tokens=max_new_tokens,
+        input_ids['input_ids'], 
+        max_length=max_new_tokens + input_ids['input_ids'].shape[1],  # Adjusting max_length to account for input length
         do_sample=True, 
         temperature=temperature,
     )
 
-    gen_text = tokenizer.decode(gen_tokens[0], skip_special_tokens=True)
-    if gen_text.startswith(user_input):
-        gen_text = gen_text[len(user_input):].lstrip()
-
+    # Decode tokens to string
+    gen_text = tokenizer.decode(gen_tokens[0])
     return gen_text
 
 
-    
 examples = [
         {"message": "What is the weather like today?", "max_new_tokens": 250, "temperature": 0.5},
         {"message": "Tell me a joke.", "max_new_tokens": 650, "temperature": 0.7},
         {"message": "Explain the concept of machine learning.", "max_new_tokens": 980, "temperature": 0.4}
 ]
-example_choices = [f"Example {i+1}" for i in range(len(examples))]
-
-def load_example(choice):
-    index = example_choices.index(choice)
-    example = examples[index]
-    return example["message"], example["max_new_tokens"], example["temperature"]
-
 
 with gr.Blocks() as demo:
     gr.Markdown(title)
@@ -57,18 +48,18 @@ with gr.Blocks() as demo:
     message_box = gr.Textbox(lines=2, label="Your Message")
     generate_button = gr.Button("Try🫡Command-R")
     output_box = gr.Textbox(label="🫡Command-R")
+    with gr.Accordion("🫡Examples", open=True):
+        gr.Examples(
+            examples=examples,
+            inputs=[message_box, max_new_tokens_slider, temperature_slider],
+            outputs=output_box,
+            fn=generate_response,
+            cache_examples=True
+        )
 
     generate_button.click(
         fn=generate_response,
         inputs=[message_box, max_new_tokens_slider, temperature_slider],
         outputs=output_box
     )
-    example_dropdown = gr.Dropdown(label="🫡Load Example", choices=example_choices)
-    example_button = gr.Button("🫡Load")
-    example_button.click(
-        fn=load_example,
-        inputs=example_dropdown,
-        outputs=[message_box, max_new_tokens_slider, temperature_slider]
-    )
-
-demo.launch()
+demo.launch()