Update

app-.py

@@ -0,0 +1,353 @@
+"""Refer to https://github.com/abacaj/mpt-30B-inference/blob/main/download_model.py."""
+# pylint: disable=invalid-name, missing-function-docstring, missing-class-docstring, redefined-outer-name, broad-except
+import os
+import time
+from dataclasses import asdict, dataclass
+
+import gradio as gr
+from ctransformers import AutoConfig, AutoModelForCausalLM
+
+# from mcli import predict
+from huggingface_hub import hf_hub_download
+from loguru import logger
+
+URL = os.environ.get("URL")
+_ = """
+if URL is None:
+    raise ValueError("URL environment variable must be set")
+if os.environ.get("MOSAICML_API_KEY") is None:
+    raise ValueError("git environment variable must be set")
+# """
+
+
+def predict0(prompt, bot, timeout):
+    logger.debug(f"{prompt=}, {bot=}, {timeout=}")
+    try:
+        user_prompt = prompt
+        generator = generate(llm, generation_config, system_prompt, user_prompt.strip())
+        print(assistant_prefix, end=" ", flush=True)
+        for word in generator:
+            print(word, end="", flush=True)
+        print("")
+        response = word
+    except Exception as exc:
+        logger.error(exc)
+        response = f"{exc=}"
+    bot = {"inputs": [response]}
+
+    return prompt, bot
+
+
+def download_mpt_quant(destination_folder: str, repo_id: str, model_filename: str):
+    local_path = os.path.abspath(destination_folder)
+    return hf_hub_download(
+        repo_id=repo_id,
+        filename=model_filename,
+        local_dir=local_path,
+        local_dir_use_symlinks=True,
+    )
+
+
+@dataclass
+class GenerationConfig:
+    temperature: float
+    top_k: int
+    top_p: float
+    repetition_penalty: float
+    max_new_tokens: int
+    seed: int
+    reset: bool
+    stream: bool
+    threads: int
+    stop: list[str]
+
+
+def format_prompt(system_prompt: str, user_prompt: str):
+    """format prompt based on: https://huggingface.co/spaces/mosaicml/mpt-30b-chat/blob/main/app.py"""
+
+    system_prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
+    user_prompt = f"<|im_start|>user\n{user_prompt}<|im_end|>\n"
+    assistant_prompt = f"<|im_start|>assistant\n"
+
+    return f"{system_prompt}{user_prompt}{assistant_prompt}"
+
+
+def generate(
+    llm: AutoModelForCausalLM,
+    generation_config: GenerationConfig,
+    system_prompt: str,
+    user_prompt: str,
+):
+    """run model inference, will return a Generator if streaming is true"""
+
+    return llm(
+        format_prompt(
+            system_prompt,
+            user_prompt,
+        ),
+        **asdict(generation_config),
+    )
+
+
+class Chat:
+    default_system_prompt = "A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers."
+    system_format = "<|im_start|>system\n{}<|im_end|>\n"
+
+    def __init__(
+        self, system: str = None, user: str = None, assistant: str = None
+    ) -> None:
+        if system is not None:
+            self.set_system_prompt(system)
+        else:
+            self.reset_system_prompt()
+        self.user = user if user else "<|im_start|>user\n{}<|im_end|>\n"
+        self.assistant = (
+            assistant if assistant else "<|im_start|>assistant\n{}<|im_end|>\n"
+        )
+        self.response_prefix = self.assistant.split("{}", maxsplit=1)[0]
+
+    def set_system_prompt(self, system_prompt):
+        # self.system = self.system_format.format(system_prompt)
+        return system_prompt
+
+    def reset_system_prompt(self):
+        return self.set_system_prompt(self.default_system_prompt)
+
+    def history_as_formatted_str(self, system, history) -> str:
+        system = self.system_format.format(system)
+        text = system + "".join(
+            [
+                "\n".join(
+                    [
+                        self.user.format(item[0]),
+                        self.assistant.format(item[1]),
+                    ]
+                )
+                for item in history[:-1]
+            ]
+        )
+        text += self.user.format(history[-1][0])
+        text += self.response_prefix
+        # stopgap solution to too long sequences
+        if len(text) > 4500:
+            # delete from the middle between <|im_start|> and <|im_end|>
+            # find the middle ones, then expand out
+            start = text.find("<|im_start|>", 139)
+            end = text.find("<|im_end|>", 139)
+            while end < len(text) and len(text) > 4500:
+                end = text.find("<|im_end|>", end + 1)
+                text = text[:start] + text[end + 1 :]
+        if len(text) > 4500:
+            # the nice way didn't work, just truncate
+            # deleting the beginning
+            text = text[-4500:]
+
+        return text
+
+    def clear_history(self, history):
+        return []
+
+    def turn(self, user_input: str):
+        self.user_turn(user_input)
+        return self.bot_turn()
+
+    def user_turn(self, user_input: str, history):
+        history.append([user_input, ""])
+        return user_input, history
+
+    def bot_turn(self, system, history):
+        conversation = self.history_as_formatted_str(system, history)
+        assistant_response = call_inf_server(conversation)
+        history[-1][-1] = assistant_response
+        print(system)
+        print(history)
+        return "", history
+
+
+def call_inf_server(prompt):
+    try:
+        response = predict(
+            URL,
+            {"inputs": [prompt], "temperature": 0.2, "top_p": 0.9, "output_len": 512},
+            timeout=70,
+        )
+        # print(f'prompt: {prompt}')
+        # print(f'len(prompt): {len(prompt)}')
+        response = response["outputs"][0]
+        # print(f'len(response): {len(response)}')
+        # remove spl tokens from prompt
+        spl_tokens = ["<|im_start|>", "<|im_end|>"]
+        clean_prompt = prompt.replace(spl_tokens[0], "").replace(spl_tokens[1], "")
+
+        # return response[len(clean_prompt) :]  # remove the prompt
+        try:
+            user_prompt = prompt
+            generator = generate(llm, generation_config, system_prompt, user_prompt.strip())
+            print(assistant_prefix, end=" ", flush=True)
+            for word in generator:
+                print(word, end="", flush=True)
+            print("")
+            response = word
+        except Exception as exc:
+            logger.error(exc)
+            response = f"{exc=}"
+        return response
+
+    except Exception as e:
+        # assume it is our error
+        # just wait and try one more time
+        print(e)
+        time.sleep(1)
+        response = predict(
+            URL,
+            {"inputs": [prompt], "temperature": 0.2, "top_p": 0.9, "output_len": 512},
+            timeout=70,
+        )
+        # print(response)
+        response = response["outputs"][0]
+        return response[len(prompt) :]  # remove the prompt
+
+
+logger.info("start dl")
+_ = """full url: https://huggingface.co/TheBloke/mpt-30B-chat-GGML/blob/main/mpt-30b-chat.ggmlv0.q4_1.bin"""
+
+repo_id = "TheBloke/mpt-30B-chat-GGML"
+model_filename = "mpt-30b-chat.ggmlv0.q4_1.bin"
+destination_folder = "models"
+
+download_mpt_quant(destination_folder, repo_id, model_filename)
+
+logger.info("done dl")
+
+config = AutoConfig.from_pretrained("mosaicml/mpt-30b-chat", context_length=8192)
+llm = AutoModelForCausalLM.from_pretrained(
+    os.path.abspath("models/mpt-30b-chat.ggmlv0.q4_1.bin"),
+    model_type="mpt",
+    config=config,
+)
+
+system_prompt = "A conversation between a user and an LLM-based AI assistant named Local Assistant. Local Assistant gives helpful and honest answers."
+
+generation_config = GenerationConfig(
+    temperature=0.2,
+    top_k=0,
+    top_p=0.9,
+    repetition_penalty=1.0,
+    max_new_tokens=512,  # adjust as needed
+    seed=42,
+    reset=False,  # reset history (cache)
+    stream=True,  # streaming per word/token
+    threads=int(os.cpu_count() / 2),  # adjust for your CPU
+    stop=["<|im_end|>", "|<"],
+)
+
+user_prefix = "[user]: "
+assistant_prefix = "[assistant]:"
+
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css=".disclaimer {font-variant-caps: all-small-caps;}",
+) as demo:
+    gr.Markdown(
+        """<h1><center>MosaicML MPT-30B-Chat</center></h1>
+
+        This demo is of [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-ch a t). It is based on [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) fine-tuned on approximately 300,000 turns of high-quality conversations, and is powered by [MosaicML Inference](https://www.mosaicml.com/inference).
+
+        If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs, [sign up](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b) for MosaicML platform.
+
+"""
+    )
+    conversation = Chat()
+    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("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(scale=2):
+                    system = gr.Textbox(
+                        label="System Prompt",
+                        value=Chat.default_system_prompt,
+                        show_label=False,
+                    ).style(container=False)
+                with gr.Column():
+                    with gr.Row():
+                        change = gr.Button("Change System Prompt")
+                        reset = gr.Button("Reset System Prompt")
+    with gr.Row():
+        gr.Markdown(
+            "Disclaimer: MPT-30B can produce factually incorrect output, and should not be relied on to produce "
+            "factually accurate information. MPT-30B 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=conversation.user_turn,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        fn=conversation.bot_turn,
+        inputs=[system, chatbot],
+        outputs=[msg, chatbot],
+        queue=True,
+    )
+    submit_click_event = submit.click(
+        fn=conversation.user_turn,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).then(
+        # fn=conversation.bot_turn,
+        inputs=[system, chatbot],
+        outputs=[msg, 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).then(
+        fn=conversation.clear_history,
+        inputs=[chatbot],
+        outputs=[chatbot],
+        queue=False,
+    )
+    change.click(
+        fn=conversation.set_system_prompt,
+        inputs=[system],
+        outputs=[system],
+        queue=False,
+    )
+    reset.click(
+        fn=conversation.reset_system_prompt,
+        inputs=[],
+        outputs=[system],
+        queue=False,
+    )
+    # """
+
+
+demo.queue(max_size=36, concurrency_count=14).launch(debug=True)

app.py

@@ -321,7 +321,6 @@ with gr.Blocks(
         outputs=[msg, chatbot],
         queue=True,
     )
-    # """
 
     stop.click(
         fn=None,
@@ -348,6 +347,7 @@ with gr.Blocks(
         outputs=[system],
         queue=False,
     )
+    # """
 
 
 demo.queue(max_size=36, concurrency_count=14).launch(debug=True)