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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed |
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from transformers import pipeline |
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import torch |
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import json |
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@st.cache(allow_output_mutation=True) |
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def load_tokenizer(model_ckpt): |
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return AutoTokenizer.from_pretrained(model_ckpt) |
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@st.cache(allow_output_mutation=True) |
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def load_model(model_ckpt): |
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model = AutoModelForCausalLM.from_pretrained(model_ckpt, low_cpu_mem_usage=True) |
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return model |
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@st.cache() |
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def load_examples(): |
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with open("examples.json", "r") as f: |
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examples = json.load(f) |
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return examples |
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st.set_page_config(page_icon=':laptop:', layout="wide") |
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st.title("Code Genaration Models comparison 💻") |
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with open("intro.txt", "r") as f: |
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intro = f.read() |
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st.markdown(intro) |
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st.sidebar.header("Models:") |
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models = ["CodeParrot", "OPT", "InCoder"] |
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selected_models = st.sidebar.multiselect('Select code generation models to compare', |
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models, |
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default=["CodeParrot"]) |
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st.sidebar.header("Tasks:") |
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tasks = ["Model architecture", "Model evaluation", "Pretraining dataset", "Prompting"] |
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selected_task = st.sidebar.selectbox("Select a task:", tasks) |
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architectures = {} |
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datasets = {} |
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pipelines = {} |
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if selected_task == "Pretraining dataset": |
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st.title("Pretraining Datasets 📚") |
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for model in selected_models: |
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with open(f"datasets/{model.lower()}.txt", "r") as f: |
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text = f.read() |
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st.markdown(f"## {model}:") |
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st.markdown(text) |
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elif selected_task == "Prompting": |
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for model in selected_models: |
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if model == "CodeParrot": |
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tokenizer = load_tokenizer("lvwerra/codeparrot") |
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model = load_model("lvwerra/codeparrot") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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pipelines[model] = pipe |
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elif model == "InCoder": |
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tokenizer = load_tokenizer("facebook/incoder-1B") |
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model = load_model("facebook/incoder-1B") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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pipelines[model] = pipe |
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else: |
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tokenizer = load_tokenizer("facebook/opt-1.3b") |
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model = load_model("facebook/opt-1.3b") |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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pipelines[model] = pipe |
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