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AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer\n","import gradio as gr\n","from threading import Thread\n","\n","MODEL_LIST = [\"prithivMLmods/Reasoning-SmolLM2-135M\", \"prithivMLmods/SmolLM2-CoT-360M\"]\n","HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n","\n","TITLE = \"

SmolLM2-Instruct

\"\n","\n","PLACEHOLDER = \"\"\"\n","
\n","

SmolLM2 is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters.

\n","
\n","\"\"\"\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","tokenizer0 = AutoTokenizer.from_pretrained(MODEL_LIST[0])\n","model0 = AutoModelForCausalLM.from_pretrained(MODEL_LIST[0]).to(device)\n","\n","tokenizer1 = AutoTokenizer.from_pretrained(MODEL_LIST[1])\n","model1 = AutoModelForCausalLM.from_pretrained(MODEL_LIST[1]).to(device)\n","\n","@spaces.GPU()\n","def stream_chat(\n"," message: str,\n"," history: list,\n"," temperature: float = 0.8,\n"," max_new_tokens: int = 1024,\n"," top_p: float = 1.0,\n"," top_k: int = 20,\n"," penalty: float = 1.2,\n"," choice: str = \"135M\"\n","):\n"," print(f'message: {message}')\n"," print(f'history: {history}')\n","\n"," conversation = []\n"," for prompt, answer in history:\n"," conversation.extend([\n"," {\"role\": \"user\", \"content\": prompt},\n"," {\"role\": \"assistant\", \"content\": answer},\n"," ])\n","\n"," conversation.append({\"role\": \"user\", \"content\": message})\n","\n"," if choice == \"135M\":\n"," tokenizer = tokenizer0\n"," model = model0\n"," elif choice == \"360M\":\n"," tokenizer = tokenizer1\n"," model = model1\n","\n"," input_text = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)\n"," inputs = tokenizer.encode(input_text, return_tensors=\"pt\").to(device)\n"," streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)\n","\n"," generate_kwargs = dict(\n"," input_ids=inputs,\n"," max_new_tokens=max_new_tokens,\n"," do_sample=False if temperature == 0 else True,\n"," top_p=top_p,\n"," top_k=top_k,\n"," temperature=temperature,\n"," streamer=streamer,\n"," )\n","\n"," with torch.no_grad():\n"," thread = Thread(target=model.generate, kwargs=generate_kwargs)\n"," thread.start()\n","\n"," buffer = \"\"\n"," for new_text in streamer:\n"," buffer += new_text\n"," yield buffer\n","\n","chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)\n","\n","with gr.Blocks(theme=\"Nymbo/Nymbo_Theme\") as demo:\n"," gr.HTML(TITLE)\n"," gr.ChatInterface(\n"," fn=stream_chat,\n"," chatbot=chatbot,\n"," fill_height=True,\n"," additional_inputs_accordion=gr.Accordion(label=\"🔼Parameters\", open=False),\n"," additional_inputs=[\n"," gr.Slider(\n"," minimum=0,\n"," maximum=1,\n"," step=0.1,\n"," value=0.8,\n"," label=\"Temperature\",\n"," ),\n"," gr.Slider(\n"," minimum=128,\n"," maximum=8192,\n"," step=1,\n"," value=1024,\n"," label=\"Max new tokens\",\n"," ),\n"," gr.Slider(\n"," minimum=0.0,\n"," maximum=1.0,\n"," step=0.1,\n"," value=1.0,\n"," label=\"top_p\",\n"," ),\n"," gr.Slider(\n"," minimum=1,\n"," maximum=20,\n"," step=1,\n"," value=20,\n"," label=\"top_k\",\n"," ),\n"," gr.Slider(\n"," minimum=0.0,\n"," maximum=2.0,\n"," step=0.1,\n"," value=1.2,\n"," label=\"Repetition penalty\",\n"," ),\n"," gr.Radio(\n"," [\"135M\", \"360M\"],\n"," value=\"360M\",\n"," label=\"Load Model\",\n"," ),\n"," ],\n"," examples=[\n"," [\"Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option.\"],\n"," [\"What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter.\"],\n"," [\"Tell me a random fun fact about the Roman Empire.\"],\n"," [\"Show me a code snippet of a website's sticky header in CSS and JavaScript.\"],\n"," ],\n"," cache_examples=False,\n"," )\n","\n","if __name__ == \"__main__\":\n"," 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