initial commit

README.md

@@ -1,6 +1,6 @@
 ---
-title: TigerLM
-emoji: 📚
+title: TigerAI-StructLM
+emoji: 🐯📏
 colorFrom: purple
 colorTo: blue
 sdk: gradio

app.py

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+import spaces
+import torch
+import sys
+import html
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from threading import Thread
+import gradio as gr
+from gradio_rich_textbox import RichTextbox
+
+
+
+title = """# 🙋🏻‍♂️Welcome to🌟Tonic's🐯📏TigerAI-StructLM-7B
+StructLM, is a series of open-source large language models (LLMs) finetuned for structured knowledge grounding (SKG) tasks. You can build with this endpoint using 🐯📏TigerAI-StructLM available here : [TIGER-Lab/StructLM-7B](https://huggingface.co/TIGER-Lab/StructLM-7B).
+You can also use 🐯📏TigerAI-StructLM by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/TigerLM?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: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) Math with [introspector](https://huggingface.co/introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [SciTonic](https://github.com/Tonic-AI/scitonic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
+"""
+assistant_message = """Use the information in the following table to solve the problem, choose between the choices if they are provided. table:"""
+system_message = "You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified."
+tabular_data = "col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0"
+user_message = "Allie kept track of how many kilometers she walked during the past 5 days. What is the range of the numbers?"
+model_name = 'TIGER-Lab/StructLM-7B'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+# model.generation_config = GenerationConfig.from_pretrained(model_name)
+# model.generation_config.pad_token_id = model.generation_config.eos_token_id
+
+@torch.inference_mode()
+@spaces.GPU
+def predict_math_bot(user_message, system_message="", assistant_message = "", tabular_data = "", max_new_tokens=125, temperature=0.1, top_p=0.9, repetition_penalty=1.9, do_sample=False):
+    prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n{assistant_message}\n\n{tabular_data}\n\n\nQuestion:\n\n{user_message}[/INST]"
+    inputs = tokenizer(prompt, return_tensors='pt', add_special_tokens=True)
+    input_ids = inputs["input_ids"].to(model.device)
+
+    output_ids = model.generate(
+        input_ids,
+        max_length=input_ids.shape[1] + max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=do_sample
+    )
+
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return response
+
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown(title)
+        with gr.Row():
+            system_message = gr.Textbox(label="📉System Prompt", placeholder=system_message)
+            assistant_message = gr.Textbox(label="Assistant Message", placeholder=assistant_message)
+            tabular_data = gr.Textbox(label="Tabular Data", placeholder=tabular_data)
+            user_message = gr.Textbox(label="🫡Enter your query here...", placeholder=user_message)
+
+        
+        with gr.Accordion("Advanced Settings"):
+            with gr.Row():
+                max_new_tokens = gr.Slider(label="Max new tokens", value=125, minimum=25, maximum=1250)
+                temperature = gr.Slider(label="Temperature", value=0.1, minimum=0.05, maximum=1.0)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99)
+                repetition_penalty = gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0)
+                do_sample = gr.Checkbox(label="Do sample", value=False)
+
+        output_text = gr.Textbox(label="🐯📏TigerAI-StructLM-7B", interactive=True)
+        
+        gr.Button("Try🫡📉MetaMath").click(
+            predict_math_bot,
+            inputs=[user_message, system_message, assistant_message, tabular_data, max_new_tokens, temperature, top_p, repetition_penalty, do_sample],
+            outputs=output_text
+        )
+             
+    demo.launch()
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+torch
+transformers
+accelerate
+bitsandbytes