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.python-version

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+gradio

README.md

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 ---
-title: African Grey Parrot Speaks To YOU
-emoji: 🏢
-colorFrom: yellow
-colorTo: gray
+title: African_Grey_Parrot_speaks_to_YOU
+app_file: app.py
 sdk: gradio
 sdk_version: 4.20.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# Load the model.
+# Note: It can take a while to download LLaMA and add the adapter modules.
+# You can also use the 13B model by loading in 4bits.
+
+import torch
+from peft import PeftModel    
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+
+model_name = "baffo32/decapoda-research-llama-7b-hf"
+adapters_name = 'timdettmers/guanaco-7b'
+
+print(f"Starting to load the model {model_name} into memory")
+
+m = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    #load_in_4bit=True,
+    torch_dtype=torch.bfloat16,
+    device_map={"": 0}
+)
+m = PeftModel.from_pretrained(m, adapters_name)
+m = m.merge_and_unload()
+tok = LlamaTokenizer.from_pretrained(model_name)
+tok.bos_token_id = 1
+
+stop_token_ids = [0]
+
+print(f"Successfully loaded the model {model_name} into memory")
+
+
+# Setup the gradio Demo.
+
+import datetime
+import os
+from threading import Event, Thread
+from uuid import uuid4
+
+import gradio as gr
+import requests
+
+max_new_tokens = 1536
+start_message = """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."""
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_id in stop_token_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+
+def convert_history_to_text(history):
+    text = start_message + "".join(
+        [
+            "".join(
+                [
+                    f"### Human: {item[0]}\n",
+                    f"### Assistant: {item[1]}\n",
+                ]
+            )
+            for item in history[:-1]
+        ]
+    )
+    text += "".join(
+        [
+            "".join(
+                [
+                    f"### Human: {history[-1][0]}\n",
+                    f"### Assistant: {history[-1][1]}\n",
+                ]
+            )
+        ]
+    )
+    return text
+
+
+def log_conversation(conversation_id, history, messages, generate_kwargs):
+    logging_url = os.getenv("LOGGING_URL", None)
+    if logging_url is None:
+        return
+
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
+
+    data = {
+        "conversation_id": conversation_id,
+        "timestamp": timestamp,
+        "history": history,
+        "messages": messages,
+        "generate_kwargs": generate_kwargs,
+    }
+
+    try:
+        requests.post(logging_url, json=data)
+    except requests.exceptions.RequestException as e:
+        print(f"Error logging conversation: {e}")
+
+
+def user(message, history):
+    # Append the user's message to the conversation history
+    return "", history + [[message, ""]]
+
+
+def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
+    print(f"history: {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 = convert_history_to_text(history)
+
+    # Tokenize the messages string
+    input_ids = tok(messages, return_tensors="pt").input_ids
+    input_ids = input_ids.to(m.device)
+    streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        do_sample=temperature > 0.0,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        streamer=streamer,
+        stopping_criteria=StoppingCriteriaList([stop]),
+    )
+
+    stream_complete = Event()
+
+    def generate_and_signal_complete():
+        m.generate(**generate_kwargs)
+        stream_complete.set()
+
+    def log_after_stream_complete():
+        stream_complete.wait()
+        log_conversation(
+            conversation_id,
+            history,
+            messages,
+            {
+                "top_k": top_k,
+                "top_p": top_p,
+                "temperature": temperature,
+                "repetition_penalty": repetition_penalty,
+            },
+        )
+
+    t1 = Thread(target=generate_and_signal_complete)
+    t1.start()
+
+    t2 = Thread(target=log_after_stream_complete)
+    t2.start()
+
+    # Initialize an empty string to store the generated text
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        history[-1][1] = partial_text
+        yield history
+
+
+def get_uuid():
+    return str(uuid4())
+
+
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css=".disclaimer {font-variant-caps: all-small-caps;}",
+) as demo:
+    conversation_id = gr.State(get_uuid)
+    gr.Markdown(
+        """<h1><center>African Grey Demo</center></h1>
+"""
+    )
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        with gr.Column():
+            msg = gr.Textbox(
+                label="Chat Message Box",
+                placeholder="Chat Message Box",
+                show_label=False,
+            )
+        with gr.Column():
+            with gr.Row():
+                submit = gr.Button("Submit")
+                stop = gr.Button("Stop")
+                clear = gr.Button("Clear")
+    with gr.Row():
+        with gr.Accordion("Advanced Options:", open=False):
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        temperature = gr.Slider(
+                            label="Temperature",
+                            value=0.7,
+                            minimum=0.0,
+                            maximum=1.0,
+                            step=0.1,
+                            interactive=True,
+                            info="Higher values produce more diverse outputs",
+                        )
+                with gr.Column():
+                    with gr.Row():
+                        top_p = gr.Slider(
+                            label="Top-p (nucleus sampling)",
+                            value=0.9,
+                            minimum=0.0,
+                            maximum=1,
+                            step=0.01,
+                            interactive=True,
+                            info=(
+                                "Sample from the smallest possible set of tokens whose cumulative probability "
+                                "exceeds top_p. Set to 1 to disable and sample from all tokens."
+                            ),
+                        )
+                with gr.Column():
+                    with gr.Row():
+                        top_k = gr.Slider(
+                            label="Top-k",
+                            value=0,
+                            minimum=0.0,
+                            maximum=200,
+                            step=1,
+                            interactive=True,
+                            info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
+                        )
+                with gr.Column():
+                    with gr.Row():
+                        repetition_penalty = gr.Slider(
+                            label="Repetition Penalty",
+                            value=1.1,
+                            minimum=1.0,
+                            maximum=2.0,
+                            step=0.1,
+                            interactive=True,
+                            info="Penalize repetition — 1.0 to disable.",
+                        )
+    with gr.Row():
+        gr.Markdown(
+            "Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce "
+            "factually accurate information. The model was trained on various public datasets; while great efforts "
+            "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
+            "biased, or otherwise offensive outputs.",
+            elem_classes=["disclaimer"],
+        )
+    with gr.Row():
+        gr.Markdown(
+            "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
+            elem_classes=["disclaimer"],
+        )
+
+    submit_event = msg.submit(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        fn=bot,
+        inputs=[
+            chatbot,
+            temperature,
+            top_p,
+            top_k,
+            repetition_penalty,
+            conversation_id,
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+    submit_click_event = submit.click(
+        fn=user,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        fn=bot,
+        inputs=[
+            chatbot,
+            temperature,
+            top_p,
+            top_k,
+            repetition_penalty,
+            conversation_id,
+        ],
+        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=128)
+
+demo.launch(share=True)

model.py

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+# Load the model.
+# Note: It can take a while to download LLaMA and add the adapter modules.
+# You can also use the 13B model by loading in 4bits.
+
+import torch
+from peft import PeftModel    
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+
+model_name = "baffo32/decapoda-research-llama-7b-hf"
+adapters_name = 'timdettmers/guanaco-7b'
+
+print(f"Starting to load the model {model_name} into memory")
+
+m = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    #load_in_4bit=True,
+    torch_dtype=torch.bfloat16,
+    device_map={"": 0}
+)
+m = PeftModel.from_pretrained(m, adapters_name)
+m = m.merge_and_unload()
+tok = LlamaTokenizer.from_pretrained(model_name)
+tok.bos_token_id = 1
+
+stop_token_ids = [0]
+
+print(f"Successfully loaded the model {model_name} into memory")

requirements.txt

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+bitsandbytes
+git+https://github.com/huggingface/transformers.git
+git+https://github.com/huggingface/peft.git
+git+https://github.com/huggingface/accelerate.git
+gradio
+sentencepiece