Update app.py

README.md

@@ -1,14 +1,12 @@
 ---
 title: Concise Reasoning Demo
-emoji: 💬
-colorFrom: yellow
+emoji: 🏍️
+colorFrom: red
 colorTo: purple
 sdk: gradio
-sdk_version: 5.0.1
+sdk_version: 5.12.0
 app_file: app.py
 pinned: false
 license: mit
 short_description: demo of LLMs fine-tuned for concise reasoning
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+---

app.py

@@ -1,64 +1,149 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+from collections.abc import Iterator
+from threading import Thread
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
+# Token limits
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 512
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Description
+DESCRIPTION = """\
+# Demo for "Self-Training Elicits Concise Reasoning in Large Language Models"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+This Space showcases the model [tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon](https://huggingface.co/tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon)
 
-    messages.append({"role": "user", "content": message})
+We provide a simple chat interface allowing you to observe the concise CoT solutions that our model can produce. Feel free to play with it.
+"""
 
-    response = ""
+# Decide on device
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n\n<p>**Warning**: Running on CPU 🥶 – this may be extremely slow. We will upgrade to GPUs soon.</p>"
+    
+# Load model and tokenizer
+model_id = "tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map=None if device == "cpu" else "auto",
+    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
+)
+model.to(device)
 
-        response += token
-        yield response
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.use_default_system_prompt = False
 
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[dict],
+    system_prompt: str = "",
+    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
+    temperature: float = 0.7,
+    top_p: float = 0.95,
+    top_k: int = 40,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    # Build conversation
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    conversation += chat_history
+    conversation.append({"role": "user", "content": message})
+    
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+    
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+    
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
+chat_interface = gr.ChatInterface(
+    fn=generate,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Textbox(label="System prompt", lines=6),
         gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
             minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.7,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
             maximum=1.0,
+            step=0.05,
             value=0.95,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=40,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
             step=0.05,
-            label="Top-p (nucleus sampling)",
+            value=1.2,
         ),
     ],
+    stop_btn=None,
+    examples=[
+        [
+            "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?"
+        ],
+        [
+            "Claire makes a 3 egg omelet every morning for breakfast.  How many dozens of eggs will she eat in 4 weeks?"
+        ],
+        [
+            "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?"
+        ],
+    ],
+    cache_examples=False,
+    type="messages",
 )
 
+with gr.Blocks(css_paths="style.css", fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+torch
+transformers

style.css

@@ -0,0 +1,12 @@
+h1 {
+    text-align: center;
+    display: block;
+  }
+  
+  #duplicate-button {
+    margin: auto;
+    color: white;
+    background: #1565c0;
+    border-radius: 100vh;
+  }
+