#from huggingface_hub import InferenceClient import gradio as gr #client = InferenceClient("""K00B404/BagOMistral_14X_Coders-ties-7B""") from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Replace 'your-username' and 'your-model-name' with your actual username and model name tokenizer = AutoTokenizer.from_pretrained('K00B404/BagOMistral_14X_Coders-ties-7B') model = AutoModelForSequenceClassification.from_pretrained('K00B404/BagOMistral_14X_Coders-ties-7B') # Example input sequence input_sequence = "This is an example sentence." # Tokenize the input sequence inputs = tokenizer(input_sequence, return_tensors="pt") # Run the input through the model outputs = model(**inputs) # Get the predicted class label predicted_class = outputs[0].argmax(-1).item() print("Predicted class:", predicted_class) """ def format_prompt(message, history): prompt = "" for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/INST]" prompt += f" {bot_response} " prompt += f"[INST] {message} [/INST]" return prompt def generate(prompt, history, temperature=0.2, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0): temperature = float(temperature) if temperature < 1e-2: temperature = 1e-2 top_p = float(top_p) generate_kwargs = dict( temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, seed=42, ) formatted_prompt = format_prompt(prompt, history) stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) output = "" for response in stream: output += response.token.text yield output return output mychatbot = gr.Chatbot(avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True) demo = gr.ChatInterface(fn=generate, chatbot=mychatbot, title="K00B404's Merged Models Test Chat", retry_btn=None, undo_btn=None ) demo.queue().launch(show_api=False) """