Upload 2 files

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+import time
+import numpy as np
+from torch.nn import functional as F
+import os
+from threading import Thread
+
+
+
+model_path = "ayoolaolafenwa/ChatLM"
+access_token = "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"
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token = access_token)
+
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code = True, device_map = "auto",
+torch_dtype=torch.bfloat16, load_in_8bit=True, use_auth_token = access_token)
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+
+def user(message, history):
+    # Append the user's message to the conversation history
+    return "", history + [[message, ""]]
+
+
+def chat(curr_system_message, history):
+    # Initialize a StopOnTokens object
+    stop = StopOnTokens()
+
+    # Construct the input message string for the model by concatenating the current system message and conversation history
+    messages = curr_system_message + \
+        "".join(["".join(["<user>: "+item[0], "<chatbot>: "+item[1]])
+                for item in history])
+        
+    # Tokenize the messages string
+    tokens = tokenizer([messages], return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(
+        tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+   
+    token_ids = tokens.input_ids
+    attention_mask=tokens.attention_mask
+    
+    generate_kwargs = dict(
+       input_ids=token_ids, 
+        attention_mask = attention_mask,  
+        streamer = streamer,
+        max_length=2048,
+        do_sample=True,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id, 
+        temperature = 0.7,
+        stopping_criteria=StoppingCriteriaList([stop])
+    ) 
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()    
+
+    #Initialize an empty string to store the generated text
+    partial_text = ""
+    for new_text in streamer:
+        # print(new_text)
+        partial_text += new_text
+        history[-1][1] = partial_text
+        # Yield an empty string to cleanup the message textbox and the updated conversation history
+        yield history
+    return partial_text
+    
+
+
+
+with gr.Blocks() as demo:
+    # history = gr.State([])
+    gr.Markdown("# ChatLM")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(
+                    """
+                    ChatLM is a chat Large Language model finetuned with pretrained [Falcon-1B model](https://huggingface.co/tiiuae/falcon-rw-1b)
+                    and trained on [chat-bot-instructions prompts dataset](https://huggingface.co/datasets/ayoolaolafenwa/sft-data).
+                    ChatLM was trained on a dataset containing normal day to day human conversations, due to limited data used in training
+                    it is not suitable for tasks like coding and current affairs. 
+                    """
+                )
+ 
+    chatbot = gr.Chatbot().style(height=500)
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box",
+                             show_label=False).style(container=False)
+        with gr.Column():
+            with gr.Row():
+                submit = gr.Button("Run")
+                stop = gr.Button("Stop")
+                clear = gr.Button("Clear")
+    system_msg = gr.Textbox(
+        label="Response Message", interactive=False, visible=False)
+
+    submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
+        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
+    submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
+        fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
+    stop.click(fn=None, inputs=None, outputs=None, cancels=[
+               submit_event, submit_click_event], queue=False)
+    clear.click(lambda: None, None, [chatbot], queue=False)
+
+
+demo.queue(max_size=32, concurrency_count=2)
+demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+torch
+transformers
+numpy
+einops
+accelerate
+bitsandbytes
+scipy