Create app.py

app.py

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+import subprocess
+from threading import Thread
+
+import torch
+import spaces
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
+
+#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+MODEL_ID = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+CHAT_TEMPLATE = "َAuto"
+MODEL_NAME = MODEL_ID.split("/")[-1]
+CONTEXT_LENGTH = 16000
+  
+
+
+latex_delimiters_set = [{
+        "left": "\\(",
+        "right": "\\)",
+        "display": False 
+    }, {
+        "left": "\\begin{equation}",
+        "right": "\\end{equation}",
+        "display": True 
+    }, {
+        "left": "\\begin{align}",
+        "right": "\\end{align}",
+        "display": True
+    }, {
+        "left": "\\begin{alignat}",
+        "right": "\\end{alignat}",
+        "display": True
+    }, {
+        "left": "\\begin{gather}",
+        "right": "\\end{gather}",
+        "display": True
+    }, {
+        "left": "\\begin{CD}",
+        "right": "\\end{CD}",
+        "display": True
+    }, {
+        "left": "\\[",
+        "right": "\\]",
+        "display": True
+    }]
+
+
+def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
+    # Format history with a given chat template
+    
+    
+    stop_tokens = ["<|endoftext|>", "<|im_end|>","|im_end|"]
+    instruction = '<|im_start|>system\n' + system_prompt + '\n<|im_end|>\n'
+    for user, assistant in history:
+        instruction += f'<|im_start|>user\n{user}\n<|im_end|>\n<|im_start|>assistant\n{assistant}\n<|im_end|>\n'
+    instruction += f'<|im_start|>user\n{message}\n<|im_end|>\n<|im_start|>assistant\n'
+    
+    print(instruction)
+    
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
+    enc = tokenizer(instruction, return_tensors="pt", padding=True, truncation=True)
+    input_ids, attention_mask = enc.input_ids, enc.attention_mask
+
+    if input_ids.shape[1] > CONTEXT_LENGTH:
+        input_ids = input_ids[:, -CONTEXT_LENGTH:]
+        attention_mask = attention_mask[:, -CONTEXT_LENGTH:]
+
+    generate_kwargs = dict(
+        input_ids=input_ids.to(device),
+        attention_mask=attention_mask.to(device),
+        streamer=streamer,
+        do_sample=True,
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        top_p=top_p
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    outputs = []
+    for new_token in streamer:
+        outputs.append(new_token)
+        if new_token in stop_tokens:
+            
+            break
+        yield "".join(outputs)
+
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+)
+
+# Create Gradio interface
+gr.ChatInterface(
+    predict,
+    
+    additional_inputs_accordion=gr.Accordion(label="Parameters", open=False),
+    additional_inputs=[
+        gr.Textbox("You are a useful assistant. first recognize user request and then reply carfuly and thinking", label="System prompt"),
+        gr.Slider(0, 1, 0.6, label="Temperature"),
+        gr.Slider(0, 32000, 10000, label="Max new tokens"),
+        gr.Slider(1, 80, 40, label="Top K sampling"),
+        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
+        gr.Slider(0, 1, 0.95, label="Top P sampling"),
+    ],
+).queue().launch()