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| # First Commit inspiration: | |
| #https://huggingface.co/spaces/lambeth-dai/Light-PDF-Web-QA-Chatbot/blob/main/app.py | |
| #--------------------- | |
| #model = AutoModelForCausalLM.from_pretrained('TheBloke/Mistral-7B-OpenOrca-GGUF', model_type='mistral', | |
| #model_file='mistral-7b-openorca.Q4_K_M.gguf', **vars(gpu_config)) | |
| #--------------------- | |
| import gradio as gr | |
| import os | |
| from ctransformers import AutoModelForCausalLM, AutoConfig, Config | |
| import datetime | |
| i_temperature = 0.30 | |
| i_max_new_tokens=1100 | |
| repo = 'TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF' | |
| model_file = "tinyllama-1.1b-1t-openorca.Q4_K_M.gguf" | |
| i_repetitionpenalty = 1.2 | |
| i_contextlength=12048 | |
| logfile = 'TinyLlamaOpenOrca1.1B-stream.txt' | |
| print("loading model...") | |
| stt = datetime.datetime.now() | |
| conf = AutoConfig(Config(temperature=i_temperature, repetition_penalty=i_repetitionpenalty, batch_size=64, | |
| max_new_tokens=i_max_new_tokens, context_length=i_contextlength)) | |
| llm = AutoModelForCausalLM.from_pretrained(repo, model_file=model_file, | |
| model_type="llama",config = conf) | |
| dt = datetime.datetime.now() - stt | |
| print(f"Model loaded in {dt}") | |
| #MODEL SETTINGS also for DISPLAY | |
| im_user = 'https://github.com/fabiomatricardi/TiniLlamaGradioChat/raw/main/456322.webp' | |
| im_bot = 'https://github.com/fabiomatricardi/TiniLlamaGradioChat/raw/main/TinyLlama_logo.png' | |
| def writehistory(text): | |
| with open(logfile, 'a', encoding='utf-8') as f: | |
| f.write(text) | |
| f.write('\n') | |
| f.close() | |
| with gr.Blocks(theme='ParityError/Interstellar') as demo: | |
| #TITLE SECTION | |
| with gr.Row(): | |
| with gr.Column(scale=12): | |
| gr.HTML("<center>" | |
| + "<h1>🦙 TinyLlama 1.1B 🐋 OpenOrca 4K context window</h2></center>") | |
| gr.Markdown(""" | |
| **Currently Running**: [tinyllama-1.1b-1t-openorca.Q4_K_M.gguf](https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF) **Chat History Log File**: *TinyLlamaOpenOrca1.1B-stream.txt* | |
| - **Base Model**: PY007/TinyLlama-1.1B-intermediate-step-480k-1T, Fine tuned on OpenOrca GPT4 subset for 1 epoch,Using CHATML format. | |
| - **License**: Apache 2.0, following the TinyLlama base model. The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk. | |
| - **Notes**: this is my first commit. So far the chat is not considering the conversation history. **Note2**: log TXT file is not working too | |
| """) | |
| gr.Image(value=im_bot, width=80) | |
| # chat and parameters settings | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| chatbot = gr.Chatbot(height = 350, show_copy_button=True, | |
| avatar_images = [im_user,im_bot]) | |
| with gr.Row(): | |
| with gr.Column(scale=14): | |
| msg = gr.Textbox(show_label=False, | |
| placeholder="Enter text", | |
| lines=2) | |
| submitBtn = gr.Button("\n💬 Send\n", size="lg", variant="primary", min_width=180) | |
| with gr.Column(min_width=50,scale=2): | |
| with gr.Tab(label="Parameter Setting"): | |
| gr.Markdown("# Parameters") | |
| top_p = gr.Slider( | |
| minimum=-0, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| interactive=True, | |
| label="Top-p", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.30, | |
| step=0.01, | |
| interactive=True, | |
| label="Temperature", | |
| ) | |
| max_length_tokens = gr.Slider( | |
| minimum=0, | |
| maximum=4096, | |
| value=1060, | |
| step=4, | |
| interactive=True, | |
| label="Max Generation Tokens", | |
| ) | |
| rep_pen = gr.Slider( | |
| minimum=0, | |
| maximum=5, | |
| value=1.2, | |
| step=0.05, | |
| interactive=True, | |
| label="Repetition Penalty", | |
| ) | |
| clear = gr.Button("🗑️ Clear All Messages", variant='secondary') | |
| def user(user_message, history): | |
| writehistory(f"USER: {user_message}") | |
| return "", history + [[user_message, None]] | |
| def bot(history,t,p,m,r): | |
| SYSTEM_PROMPT = """<|im_start|>system | |
| You are a helpful bot. Your answers are clear and concise. | |
| <|im_end|> | |
| """ | |
| prompt = f"<|im_start|>system<|im_end|><|im_start|>user\n{history[-1][0]}<|im_end|>\n<|im_start|>assistant\n" | |
| print(f"history lenght: {len(history)}") | |
| if len(history) == 1: | |
| print("this is the first round") | |
| else: | |
| print("here we should pass more conversations") | |
| history[-1][1] = "" | |
| for character in llm(prompt, | |
| temperature = t, | |
| top_p = p, | |
| repetition_penalty = r, | |
| max_new_tokens=m, | |
| stop = ['<|im_end|>'], | |
| stream = True): | |
| history[-1][1] += character | |
| yield history | |
| writehistory(f"temperature: {t}, top_p: {p}, maxNewTokens: {m}, repetitionPenalty: {r}\n---\nBOT: {history}\n\n") | |
| #Log in the terminal the messages | |
| print(f"USER: {history[-1][0]}\n---\ntemperature: {t}, top_p: {p}, maxNewTokens: {m}, repetitionPenalty: {r}\n---\nBOT: {history[-1][1]}\n\n") | |
| # Clicking the submitBtn will call the generation with Parameters in the slides | |
| submitBtn.click(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, [chatbot,temperature,top_p,max_length_tokens,rep_pen], chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| demo.queue() #required to yield the streams from the text generation | |
| demo.launch(inbrowser=True) |