Spaces:
Sleeping
Sleeping
Duplicate from joaogante/transformers_streaming
Browse filesCo-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>
- .gitattributes +34 -0
- .gitignore +169 -0
- README.md +13 -0
- app.py +89 -0
- requirements.txt +4 -0
.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore
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# Initially taken from Github's Python gitignore file
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# tests and logs
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tests/fixtures/cached_*_text.txt
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logs/
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lightning_logs/
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lang_code_data/
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# Distribution / packaging
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.Python
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build/
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dist/
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downloads/
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*.egg
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MANIFEST
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# PyInstaller
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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# Installer logs
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pip-delete-this-directory.txt
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htmlcov/
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.nox/
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coverage.xml
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profile_default/
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ipython_config.py
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env/
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venv/
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ENV/
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# vscode
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.vs
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.vscode
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# Pycharm
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.idea
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# TF code
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tensorflow_code
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# Models
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proc_data
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# examples
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runs
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/runs_old
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/wandb
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/examples/**/*.args
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/examples/rag/sweep
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# data
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/data
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serialization_dir
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# emacs
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*.*~
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debug.env
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#ctags
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tags
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# pre-commit
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.pre-commit*
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# .lock
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*.lock
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# DS_Store (MacOS)
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.DS_Store
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# ruff
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.ruff_cache
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README.md
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---
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title: Chatbot Transformers Streaming
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emoji: 👀
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colorFrom: gray
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colorTo: blue
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sdk: gradio
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sdk_version: 3.23.0
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app_file: app.py
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pinned: false
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duplicated_from: joaogante/transformers_streaming
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from threading import Thread
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import torch
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TextIteratorStreamer
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model_id = "declare-lab/flan-alpaca-large"
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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print("Running on device:", torch_device)
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print("CPU threads:", torch.get_num_threads())
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if torch_device == "cuda":
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto")
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else:
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
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# Get the model and tokenizer, and tokenize the user text.
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model_inputs = tokenizer([user_text], return_tensors="pt").to(torch_device)
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# Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
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# in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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model_inputs,
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streamer=streamer,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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top_p=top_p,
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temperature=float(temperature),
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top_k=top_k
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)
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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# Pull the generated text from the streamer, and update the model output.
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model_output = ""
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for new_text in streamer:
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model_output += new_text
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yield model_output
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return model_output
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def reset_textbox():
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return gr.update(value='')
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with gr.Blocks() as demo:
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duplicate_link = "https://huggingface.co/spaces/joaogante/transformers_streaming?duplicate=true"
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gr.Markdown(
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"# 🤗 Transformers 🔥Streaming🔥 on Gradio\n"
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"This demo showcases the use of the "
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"[streaming feature](https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming) "
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"of 🤗 Transformers with Gradio to generate text in real-time. It uses "
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f"[{model_id}](https://huggingface.co/{model_id}) and the Spaces free compute tier.\n\n"
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f"Feel free to [duplicate this Space]({duplicate_link}) to try your own models or use this space as a "
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"template! 💛"
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)
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with gr.Row():
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with gr.Column(scale=4):
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user_text = gr.Textbox(
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placeholder="Write an email about an alpaca that likes flan",
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label="User input"
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)
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model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
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button_submit = gr.Button(value="Submit")
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with gr.Column(scale=1):
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max_new_tokens = gr.Slider(
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minimum=1, maximum=1000, value=250, step=1, interactive=True, label="Max New Tokens",
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)
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top_p = gr.Slider(
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minimum=0.05, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
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)
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top_k = gr.Slider(
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minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k",
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)
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temperature = gr.Slider(
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minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature",
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)
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user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
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button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
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demo.queue(max_size=32).launch(enable_queue=True)
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requirements.txt
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accelerate
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bitsandbytes
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torch
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git+https://github.com/huggingface/transformers.git # transformers from main (TextIteratorStreamer will be added in v4.28)
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