Update app.py

app.py

@@ -1,9 +1,13 @@
 import gradio as gr
 import spaces
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 import torch
+from threading import Thread
+from typing import Iterator
 
 model_name = "rubenroy/Zurich-14B-GCv2-5m"
+MAX_INPUT_TOKEN_LENGTH = 4096
+
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype=torch.bfloat16,
@@ -12,31 +16,41 @@ model = AutoModelForCausalLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 @spaces.GPU
-def generate(message, chat_history, temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=512, repetition_penalty=1.1):
-    messages = [
-        {"role": "system", "content": "You are a helpul assistant named Zurich, a 7 billion parameter Large Language model, you were fine-tuned and trained by Ruben Roy. You have been trained with the GammaCorpus v2 dataset, a dataset filled with structured and filtered multi-turn conversations, this was also made by Ruben Roy."},
-        {"role": "user", "content": message}
-    ]
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-    generated_ids = model.generate(
-        **model_inputs,
-        temperature=float(temperature),
-        top_p=float(top_p),
-        top_k=int(top_k),
-        max_new_tokens=int(max_new_tokens),
-        repetition_penalty=float(repetition_penalty),
-        do_sample=True if float(temperature) > 0 else False
+def generate(message: str, chat_history: list[tuple[str, str]], temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=512, repetition_penalty=1.1) -> Iterator[str]:
+    """Generates text responses using Zurich model with streaming."""
+    
+    conversation = []
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    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 if float(temperature) > 0 else False,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty
     )
-    generated_ids = [
-        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-    ]
-    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    return response
+    
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
 
 TITLE_HTML = """
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
@@ -69,85 +83,12 @@ TITLE_HTML = """
         backdrop-filter: blur(10px);
         transition: all 0.3s ease;
     }
-    .info-link {
-        color: #60a5fa;
-        text-decoration: none;
-        transition: color 0.2s ease;
-    }
-    .info-link:hover {
-        color: #93c5fd;
-        text-decoration: underline;
-    }
-    .info-section {
-        margin-top: 0.5rem;
-        font-size: 0.9rem;
-        color: #94a3b8;
-    }
-    .settings-section {
-        background: rgba(255, 255, 255, 0.05);
-        padding: 1.5rem;
-        border-radius: 1rem;
-        margin: 1.5rem auto;
-        border: 1px solid rgba(255, 255, 255, 0.1);
-        max-width: 800px;
-    }
-    .settings-title {
-        color: #e2e8f0;
-        font-size: 1.25rem;
-        font-weight: 600;
-        margin-bottom: 1rem;
-        display: flex;
-        align-items: center;
-        gap: 0.7rem;
-    }
-    .parameter-info {
-        color: #94a3b8;
-        font-size: 0.8rem;
-        margin-top: 0.25rem;
-    }
 </style>
 
 <div style="background: linear-gradient(135deg, #1e293b 0%, #0f172a 100%); padding: 1.5rem; border-radius: 1.5rem; text-align: center; margin: 1rem auto; max-width: 1200px; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);">
     <div style="margin-bottom: 1.5rem;">
-        <div style="display: flex; align-items: center; justify-content: center; gap: 1rem;">
-            <h1 style="font-size: 2.5rem; font-weight: 800; margin: 0; background: linear-gradient(135deg, #60a5fa 0%, #93c5fd 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Zurich</h1>
-            <div style="width: 2px; height: 2.5rem; background: linear-gradient(180deg, #3b82f6 0%, #60a5fa 100%);"></div>
-            <p style="font-size: 1.25rem; color: #94a3b8; margin: 0;">GammaCorpus v2-5m</p>
-        </div>
-        <div class="info-section">
-            <span>Fine-tuned from <a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct" class="info-link">Qwen 2.5 14B Instruct</a> | Model: <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="info-link">Zurich-14B-GCv2-5m</a> | Training Dataset: <a href="https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-5m" class="info-link">GammaCorpus v2 5m</a></span>
-        </div>
-    </div>
-
-    <div style="display: flex; gap: 1.5rem; justify-content: center;">
-        <div class="model-section">
-            <h2 style="font-size: 1.25rem; color: #e2e8f0; margin-bottom: 1.4rem; margin-top: 1px; font-weight: 600; display: flex; align-items: center; justify-content: center; gap: 0.7rem;">
-                <i class="fas fa-brain"></i>
-                7B Models
-            </h2>
-            <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;">
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-5m" class="model-btn">Zurich 7B GCv2 5m</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-1m" class="model-btn">Zurich 7B GCv2 1m</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-500k" class="model-btn">Zurich 7B GCv2 500k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-100k" class="model-btn">Zurich 7B GCv2 100k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-50k" class="model-btn">Zurich 7B GCv2 50k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-10k" class="model-btn">Zurich 7B GCv2 10k</a>
-            </div>
-        </div>
-        <div class="model-section">
-            <h2 style="font-size: 1.25rem; color: #e2e8f0; margin-bottom: 1.4rem; margin-top: 1px; font-weight: 600; display: flex; align-items: center; justify-content: center; gap: 0.7rem;">
-                <i class="fas fa-rocket"></i>
-                14B Models
-            </h2>
-            <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;">
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="model-btn">Zurich 14B GCv2 5m</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-1m" class="model-btn">Zurich 14B GCv2 1m</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-500k" class="model-btn">Zurich 14B GCv2 500k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-100k" class="model-btn">Zurich 14B GCv2 100k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-50k" class="model-btn">Zurich 14B GCv2 50k</a>
-                <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-10k" class="model-btn">Zurich 14B GCv2 10k</a>
-            </div>
-        </div>
+        <h1 style="font-size: 2.5rem; font-weight: 800; margin: 0; background: linear-gradient(135deg, #60a5fa 0%, #93c5fd 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Zurich</h1>
+        <p style="font-size: 1.25rem; color: #94a3b8; margin: 0;">GammaCorpus v2-5m</p>
     </div>
 </div>
 """
@@ -156,7 +97,7 @@ examples = [
     ["Explain quantum computing in simple terms"],
     ["Write a short story about a time traveler"],
     ["Explain the process of photosynthesis"],
-    ["Tell me an intersting fact about Palm trees"]
+    ["Tell me an interesting fact about Palm trees"]
 ]
 
 with gr.Blocks() as demo:
@@ -165,62 +106,16 @@ with gr.Blocks() as demo:
     with gr.Accordion("Generation Settings", open=False):
         with gr.Row():
             with gr.Column():
-                temperature = gr.Slider(
-                    minimum=0.0,
-                    maximum=2.0,
-                    value=0.7,
-                    step=0.1,
-                    label="Temperature",
-                    info="Higher values make the output more random, lower values make it more deterministic",
-                    interactive=True
-                )
-                top_p = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.9,
-                    step=0.05,
-                    label="Top P",
-                    info="Controls the cumulative probability threshold for nucleus sampling",
-                    interactive=True
-                )
-                top_k = gr.Slider(
-                    minimum=1,
-                    maximum=100,
-                    value=50,
-                    step=1,
-                    label="Top K",
-                    info="Limits the number of tokens to consider for each generation step",
-                    interactive=True
-                )
+                temperature = gr.Slider(0.0, 2.0, value=0.7, step=0.1, label="Temperature", info="Higher values make the output more random")
+                top_p = gr.Slider(0.0, 1.0, value=0.9, step=0.05, label="Top P", info="Controls nucleus sampling")
+                top_k = gr.Slider(1, 100, value=50, step=1, label="Top K", info="Limits vocabulary choices per step")
             with gr.Column():
-                max_new_tokens = gr.Slider(
-                    minimum=1,
-                    maximum=2048,
-                    value=512,
-                    step=1,
-                    label="Max New Tokens",
-                    info="Maximum number of tokens to generate in the response",
-                    interactive=True
-                )
-                repetition_penalty = gr.Slider(
-                    minimum=1.0,
-                    maximum=2.0,
-                    value=1.1,
-                    step=0.1,
-                    label="Repetition Penalty",
-                    info="Higher values stop the model from repeating the same info",
-                    interactive=True
-                )
-    
+                max_new_tokens = gr.Slider(1, 2048, value=512, step=1, label="Max New Tokens", info="Limits response length")
+                repetition_penalty = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty", info="Discourages repeated phrases")
+
     chatbot = gr.ChatInterface(
         fn=generate,
-        additional_inputs=[
-            temperature,
-            top_p,
-            top_k,
-            max_new_tokens,
-            repetition_penalty
-        ],
+        additional_inputs=[temperature, top_p, top_k, max_new_tokens, repetition_penalty],
         examples=examples
     )