Create app.py

app.py

@@ -0,0 +1,79 @@
+import spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import LlamaTokenizerFast
+import torch
+import sentencepiece
+import os
+import gradio as gr
+
+
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:120'
+model_id = "eastwind/grok-1-hf-4bit"
+tokenizer_id = "Xenova/grok-1-tokenizer"
+# tokenizer_path = "./"
+# eos_token_id = 7
+
+DESCRIPTION = """
+# Welcome to Tonic's Grok-1
+"""
+
+#tokenizer = AutoTokenizer.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
+tokenizer = LlamaTokenizerFast.from_pretrained(tokenizer_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
+
+def format_prompt(user_message, system_message="You are Grok-1, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and follow ethical guidelines and promote positive behavior.\n\n"):
+#   prompt = f"<|im_start|>assistant\n{system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n"
+    prompt = f"{system_message}{user_message}"
+    return prompt
+
+@spaces.GPU
+def predict(message, system_message, max_new_tokens=600, temperature=3.5, top_p=0.9, top_k=40, do_sample=False):
+    formatted_prompt = format_prompt(message, system_message)
+
+    input_ids = tokenizer.encode(formatted_prompt, return_tensors='pt')
+    input_ids = input_ids.to(model.device)
+
+    response_ids = model.generate(
+        input_ids,
+        max_length=max_new_tokens + input_ids.shape[1],
+        temperature=temperature,  
+        top_p=top_p,              
+        top_k=top_k,              
+        no_repeat_ngram_size=9,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=do_sample
+    )
+
+    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    truncate_str = "<|im_end|>"
+    if truncate_str and truncate_str in response:
+        response = response.split(truncate_str)[0]
+
+    return [("bot", response)]    
+    
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        textbox = gr.Textbox(placeholder='Your Message Here', label='Your Message', lines=2)
+        system_prompt = gr.Textbox(placeholder='Provide a System Prompt In The First Person', label='System Prompt', lines=2, value="You are YiTonic, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.")
+
+    with gr.Group():
+        chatbot = gr.Chatbot(label='Grok-1🤯')
+
+    with gr.Group():
+        submit_button = gr.Button('Submit', variant='primary')
+
+    with gr.Accordion(label='Advanced options', open=False):
+        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=55000, step=1, value=4056)
+        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=1.2)
+        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=40)
+        do_sample_checkbox = gr.Checkbox(label='Disable for faster inference', value=True)
+
+    submit_button.click(
+        fn=predict,
+        inputs=[textbox, system_prompt, max_new_tokens, temperature, top_p, top_k, do_sample_checkbox],
+        outputs=chatbot
+    )
+
+demo.launch()