import gradio as gr from huggingface_hub import InferenceClient client = InferenceClient("google/gemma-1.1-2b-it") client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407") def models(Query): messages = [] messages.append({"role": "user", "content": f"[SYSTEM] You are ASSISTANT who answer question asked by user in short and concise manner. [USER] {Query}"}) Response = "" for message in client.chat_completion( messages, max_tokens=2048, stream=True ): token = message.choices[0].delta.content Response += token yield Response def nemo(query): budget = 3 message = f"""[INST] [SYSTEM] You are a helpful assistant in normal conversation. When given a problem to solve, you are an expert problem-solving assistant. Your task is to provide a detailed, step-by-step solution to a given question. Follow these instructions carefully: 1. Read the given question carefully and reset counter between <count> and </count> to {budget} (maximum 3 steps). 2. Think critically like a human researcher or scientist. Break down the problem using first principles to conceptually understand and answer the question. 3. Generate a detailed, logical step-by-step solution. 4. Enclose each step of your solution within <step> and </step> tags. 5. You are allowed to use at most {budget} steps (starting budget), keep track of it by counting down within tags <count> </count>, STOP GENERATING MORE STEPS when hitting 0, you don't have to use all of them. 6. Do a self-reflection when you are unsure about how to proceed, based on the self-reflection and reward, decide whether you need to return to the previous steps. 7. After completing the solution steps, reorganize and synthesize the steps into the final answer within <answer> and </answer> tags. 8. Provide a critical, honest, and subjective self-evaluation of your reasoning process within <reflection> and </reflection> tags. 9. Assign a quality score to your solution as a float between 0.0 (lowest quality) and 1.0 (highest quality), enclosed in <reward> and </reward> tags. Example format: <count> [starting budget] </count> <step> [Content of step 1] </step> <count> [remaining budget] </count> <step> [Content of step 2] </step> <reflection> [Evaluation of the steps so far] </reflection> <reward> [Float between 0.0 and 1.0] </reward> <count> [remaining budget] </count> <step> [Content of step 3 or Content of some previous step] </step> <count> [remaining budget] </count> ... <step> [Content of final step] </step> <count> [remaining budget] </count> <answer> [Final Answer] </answer> (must give final answer in this format) <reflection> [Evaluation of the solution] </reflection> <reward> [Float between 0.0 and 1.0] </reward> [/INST] [INST] [QUERY] {query} [/INST] [ASSISTANT] """ stream = client.text_generation(message, max_new_tokens=4096, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text return output description="# Chat GO\n### Enter your query and Press enter and get lightning fast response" with gr.Blocks() as demo1: gr.Interface(description=description,fn=models, inputs=["text"], outputs="text") with gr.Blocks() as demo2: gr.Interface(description="Very low but critical thinker",fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10) with gr.Blocks() as demo: gr.TabbedInterface([demo1, demo2] , ["Fast", "Critical"]) demo.queue(max_size=300000) demo.launch()