Initial test

.streamlit/config.toml

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+[theme]
+base="dark"

app.py

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+import time  # to simulate a real time data, time loop
+from os import listdir
+from os.path import isfile, join
+import numpy as np  # np mean, np random
+import pandas as pd  # read csv, df manipulation
+import plotly.express as px  # interactive charts
+from plotly import graph_objs as go
+import streamlit as st  # 🎈 data web app development
+import plotly.figure_factory as ff
+import numpy as np
+from collections import Counter
+
+
+print("Make sure to activate your VPN before running this script")
+
+st.set_page_config(
+    page_title="GroqFlow Progress Tracker",
+    page_icon="🚀",
+    layout="wide",
+)
+
+
+# Session State variables:
+state = st.session_state
+if "INFO_CLOSED" not in state:
+    state.INFO_CLOSED = False
+
+# dashboard title
+st.title("GroqFlow Progress Tracker 🚀")
+
+# Custom chart colors (https://plotly.com/python/discrete-color/)
+colorway = ["#3366cc", "#FF7F0E"]
+
+
+def add_filter(data_frame_list, name, label, options, num_cols=1):
+    st.markdown(f"#### {name}")
+
+    cols = st.columns(num_cols)
+    instantiated_checkbox = []
+    for idx in range(len(options)):
+        with cols[idx % num_cols]:
+            instantiated_checkbox.append(st.checkbox(options[idx], False))
+
+    all_options = set(data_frame_list[-1][label])
+    selected_options = [
+        options[idx] for idx, checked in enumerate(instantiated_checkbox) if checked
+    ]
+
+    # The last checkbox will always correspond to "other"
+    if instantiated_checkbox[-1]:
+        selected_options = selected_options[:-1]
+        other_options = [x for x in all_options if x not in options]
+        selected_options = set(selected_options + other_options)
+
+    if len(selected_options) > 0:
+        for idx in range(len(data_frame_list)):
+            data_frame_list[idx] = data_frame_list[idx][
+                [
+                    any([x == model_entry for x in selected_options])
+                    for model_entry in data_frame_list[idx][label]
+                ]
+            ]
+    return data_frame_list
+
+
+with st.sidebar:
+
+    st.markdown("# Filters")
+
+    test_type = st.radio(
+        "Test Type",
+        ("Daily Tests (100 models)", "Monthly Tests (500+ models)"),
+    )
+    if test_type == "Daily Tests (100 models)":
+        selected_test_type = "daily"
+        report_folder = "reports/daily"
+    else:
+        selected_test_type = "monthly"
+        report_folder = "reports/monthly"
+
+    # Get ML Agility reports
+    reports = sorted(
+        [f for f in listdir(report_folder) if isfile(join(report_folder, f))]
+    )
+
+    selected_report = st.selectbox("Test date", reports, index=len(reports) - 1)
+    selected_report_idx = reports.index(selected_report)
+    prev_report = reports[max(0, selected_report_idx - 1)]
+    mla_report = pd.read_csv(f"{report_folder}/{selected_report}")
+    prev_mla_report = pd.read_csv(f"{report_folder}/{prev_report}")
+
+    # Add chips filter
+    num_chips_options = ["1", "2", "4", "8", "16", "32+"]
+    mla_report = mla_report.astype({"chips_used": str})
+    prev_mla_report = prev_mla_report.astype({"chips_used": str})
+    mla_report, prev_mla_report = add_filter(
+        [mla_report, prev_mla_report],
+        "Number of GroqChips™",
+        label="chips_used",
+        options=num_chips_options,
+        num_cols=3,
+    )
+
+    # Add author filter
+    authors = [
+        "google",
+        "apple",
+        "facebook",
+        "openai",
+        "microsoft",
+        "huggingface",
+        "CompVis",
+        "others",
+    ]
+    mla_report, prev_mla_report = add_filter(
+        [mla_report, prev_mla_report],
+        "Authors",
+        label="author",
+        options=authors,
+        num_cols=2,
+    )
+
+    # Add task filter
+    tasks = [
+        "Image Classification",
+        "Translation",
+        "Image Segmentation",
+        "Fill-Mask",
+        "Text-to-Image",
+        "Token Classification",
+        "Sentence Similarity",
+        "Audio Classification",
+        "Question Answering",
+        "Summarization",
+        "other",
+    ]
+    mla_report, prev_mla_report = add_filter(
+        [mla_report, prev_mla_report], "Tasks", label="task", options=tasks
+    )
+
+
+def detailed_progress_list(df_new, df_old, filter=None):
+    return
+    """
+    if filter is not None:
+        df_new = df_new[(df_new[filter] == True)]
+        df_old = df_old[(df_old[filter] == True)]
+
+    progress = df_new[~(df_new["hash"].isin(df_old["hash"]))].reset_index(drop=True)
+    regression = df_old[~(df_old["hash"].isin(df_new["hash"]))].reset_index(drop=True)
+
+    for model_name in progress["model_name"]:
+        st.markdown(
+            f'<span style="color:green">↑ {model_name}</span>',
+            unsafe_allow_html=True,
+        )
+    for model_name in regression["model_name"]:
+        st.markdown(
+            f'<span style="color:red">↓ {model_name}</span>',
+            unsafe_allow_html=True,
+        )
+    """
+
+
+# creating a single-element container
+placeholder = st.empty()
+
+with placeholder.container():
+
+    st.markdown("## Summary Results")
+    # create three columns
+    kpi = st.columns(7)
+    model_details = st.columns(7)
+
+    # fill in those three columns with respective metrics or KPIs
+    kpi[0].metric(
+        label="All models",
+        value=len(mla_report),
+        delta=len(mla_report) - len(prev_mla_report),
+    )
+    if selected_test_type == "daily":
+        with model_details[0]:
+            detailed_progress_list(mla_report, prev_mla_report)
+
+    kpi[1].metric(
+        label="Convert to ONNX",
+        value=np.sum(mla_report["base_onnx"]),
+        delta=int(
+            np.sum(mla_report["base_onnx"]) - np.sum(prev_mla_report["base_onnx"])
+        ),
+    )
+    if selected_test_type == "daily":
+        with model_details[1]:
+            detailed_progress_list(mla_report, prev_mla_report, "base_onnx")
+
+    kpi[2].metric(
+        label="Optimize ONNX file",
+        value=np.sum(mla_report["optimized_onnx"]),
+        delta=int(
+            np.sum(mla_report["optimized_onnx"])
+            - np.sum(prev_mla_report["optimized_onnx"])
+        ),
+    )
+    if selected_test_type == "daily":
+        with model_details[2]:
+            detailed_progress_list(mla_report, prev_mla_report, "optimized_onnx")
+
+    kpi[3].metric(
+        label="All ops supported",
+        value=np.sum(mla_report["all_ops_supported"]),
+        delta=int(
+            np.sum(mla_report["all_ops_supported"])
+            - np.sum(prev_mla_report["all_ops_supported"])
+        ),
+    )
+    if selected_test_type == "daily":
+        with model_details[3]:
+            detailed_progress_list(mla_report, prev_mla_report, "all_ops_supported")
+
+    kpi[4].metric(
+        label="Converts to FP16",
+        value=np.sum(mla_report["fp16_onnx"]),
+        delta=int(
+            np.sum(mla_report["fp16_onnx"]) - np.sum(prev_mla_report["fp16_onnx"])
+        ),
+    )
+    if selected_test_type == "daily":
+        with model_details[4]:
+            detailed_progress_list(mla_report, prev_mla_report, "fp16_onnx")
+
+    kpi[5].metric(
+        label="Compiles",
+        value=np.sum(mla_report["compiles"]),
+        delta=int(np.sum(mla_report["compiles"]) - np.sum(prev_mla_report["compiles"])),
+    )
+    if selected_test_type == "daily":
+        with model_details[5]:
+            detailed_progress_list(mla_report, prev_mla_report, "compiles")
+
+    kpi[6].metric(
+        label="Assembles",
+        value=np.sum(mla_report["assembles"]),
+        delta=int(
+            np.sum(mla_report["assembles"]) - np.sum(prev_mla_report["assembles"])
+        ),
+    )
+    if selected_test_type == "daily":
+        with model_details[6]:
+            detailed_progress_list(mla_report, prev_mla_report, "assembles")
+
+    cols = st.columns(2)
+    with cols[0]:
+
+        compiler_errors = mla_report[mla_report["compiler_error"] != "-"][
+            "compiler_error"
+        ]
+        compiler_errors = Counter(compiler_errors)
+        st.markdown("""#### Top compiler issues""")
+        if len(compiler_errors) > 0:
+            compiler_errors = pd.DataFrame.from_dict(
+                compiler_errors, orient="index"
+            ).reset_index()
+            compiler_errors = compiler_errors.set_axis(
+                ["error", "count"], axis=1, inplace=False
+            )
+
+            fig = px.bar(
+                compiler_errors, x="count", y="error", orientation="h", height=400
+            )
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.markdown("""No compiler errors found :tada:""")
+
+    with cols[1]:
+        # Add parameters histogram
+        all_models = [float(x) / 1000000 for x in mla_report["params"] if x != "-"]
+
+        assembled_models = mla_report[mla_report["assembles"] == True]
+        assembled_models = [
+            float(x) / 1000000 for x in assembled_models["params"] if x != "-"
+        ]
+        hist_data = []
+        group_labels = []
+        if all_models != []:
+            hist_data.append(all_models)
+            group_labels.append("Models we tried compiling")
+
+        if assembled_models != []:
+            hist_data.append(assembled_models)
+            group_labels.append("Assembled models")
+
+        st.markdown("""#### Assembled models vs. Parameters (in millions)""")
+
+        if len(assembled_models) > 1:
+
+            fig = ff.create_distplot(
+                hist_data,
+                group_labels,
+                bin_size=[25, 25],
+                histnorm="",
+            )
+            # fig.layout.update(title="Assembled models vs. Parameters (in millions)")
+            fig.layout.update(xaxis_title="Parameters in millions")
+            fig.layout.update(yaxis_title="count")
+            fig.update_xaxes(range=[1, 1000])
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.markdown("""Need at least one assembled model to show this graph 😅""")
+
+    if "tsp_gpu_compute_ratio" in mla_report and "tsp_gpu_e2e_ratio" in mla_report:
+        cols = st.columns(2)
+        with cols[0]:
+            # GPU Acceleration plot
+            st.markdown("""#### Speedup of GroqChip™ compared to A100 GPUs""")
+
+            # Prepare data
+            df = mla_report[
+                ["model_name", "tsp_gpu_compute_ratio", "tsp_gpu_e2e_ratio"]
+            ]
+            df = df.sort_values(by=["model_name"])
+            df = df[(df.tsp_gpu_compute_ratio != "-")]
+            df = df[(df.tsp_gpu_e2e_ratio != "-")]
+            df["tsp_gpu_compute_ratio"] = df["tsp_gpu_compute_ratio"].astype(float)
+            df["tsp_gpu_e2e_ratio"] = df["tsp_gpu_e2e_ratio"].astype(float)
+
+            data = [
+                go.Bar(
+                    x=df["model_name"],
+                    y=df["tsp_gpu_compute_ratio"],
+                    name="Compute only",
+                ),
+                go.Bar(
+                    x=df["model_name"],
+                    y=df["tsp_gpu_e2e_ratio"],
+                    name="Compute + estimated I/O",
+                ),
+            ]
+
+            layout = go.Layout(
+                barmode="overlay",
+                yaxis_title="Speedup compared to A100 GPU",
+                colorway=colorway,
+            )
+
+            fig = dict(data=data, layout=layout)
+            st.plotly_chart(fig, use_container_width=True)
+
+            st.markdown(
+                "<sup>*</sup>Estimated I/O does NOT include delays caused by Groq's runtime.",
+                unsafe_allow_html=True,
+            )
+
+        with cols[1]:
+            # Show stats
+            st.markdown(
+                f"""<br><br><br><br><br><br>
+                <p style="font-family:sans-serif; font-size: 20px;text-align: center;">Average speedup of GroqChip™ considering compute only:</p>
+                <p style="font-family:sans-serif; color:#3366cc; font-size: 26px;text-align: center;"> {round(df["tsp_gpu_compute_ratio"].mean(),2)}x</p>
+                <p style="font-family:sans-serif; color:#3366cc; font-size: 20px;text-align: center;"> min {round(df["tsp_gpu_compute_ratio"].min(),2)}x; max {round(df["tsp_gpu_compute_ratio"].max(),2)}x</p>
+                <br><br>
+                <p style="font-family:sans-serif; font-size: 20px;text-align: center;">Average speedup of GroqChip™ considering compute + estimated I/O<sup>*</sup>:</p>
+                <p style="font-family:sans-serif; color:#FF7F0E; font-size: 26px;text-align: center;"> {round(df["tsp_gpu_e2e_ratio"].mean(),2)}x</p>
+                <p style="font-family:sans-serif; color:#FF7F0E; font-size: 20px;text-align: center;"> min {round(df["tsp_gpu_e2e_ratio"].min(),2)}x; max {round(df["tsp_gpu_e2e_ratio"].max(),2)}x</p>""",
+                unsafe_allow_html=True,
+            )
+
+    st.markdown("### Detailed Data View")
+    st.markdown(
+        "**Model selection**: All workloads were obtained from models cards available at huggingface.co/models. Input shapes corresponds exactly to those used by the Huggingface model cards. Some of those input shapes might be small, causing the compilation process to be easier than when reasonably-sized input shapes are used.",
+        unsafe_allow_html=True,
+    )
+    model_name = st.text_input("", placeholder="Filter model by name")
+    if model_name != "":
+        mla_report = mla_report[[model_name in x for x in mla_report["model_name"]]]
+
+    # Select which columns to show
+    selected_cols = list(mla_report.columns)
+    # remove_cols = (
+    #    "tsp_e2e_latency",
+    #    "gpu_e2e_latency",
+    #    "tsp_gpu_e2e_ratio",
+    # )
+    # for item in remove_cols:
+    #    if item in selected_cols:
+    #        selected_cols.remove(item)
+    st.dataframe(
+        mla_report[selected_cols], height=min((len(mla_report) + 1) * 35, 35 * 21)
+    )

reports/daily/2023-01-01.csv

@@ -0,0 +1,112 @@
+model_name,author,class,downloads,base_onnx,optimized_onnx,all_ops_supported,fp16_onnx,compiles,assembles,params,chips_used,hash,license,task,model_type,cycles,tsp_compute_latency,gpu_compute_latency,tsp_gpu_compute_ratio,tsp_estimated_e2e_latency,gpu_e2e_latency,tsp_gpu_e2e_ratio,compiler_error,export_time,optimize_onnx_time,check_compatibility_time,fp16_conversion_time,compile_time,assemble_time,compiler_ram_GB
+ldm-text2im-large-256,CompVis,LDMBertModel,2736,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,542895638,0,5a193210,apache-2.0,Text-to-Image,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+ldm-text2im-large-256,CompVis,UNet2DConditionModel,2736,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,872305830,0,d4c354d4,apache-2.0,Text-to-Image,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+stable-diffusion-v1-4,CompVis,UNet2DConditionModel,933179,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,859526310,0,8d97aa42,creativeml-openrail-m,Text-to-Image,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+stable-diffusion-v1-4,CompVis,CLIPTextModel,933179,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,123066514,0,d312ecd1,creativeml-openrail-m,Text-to-Image,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deeplabv3-mobilevit-small,apple,MobileViTForSemanticSegmentation,623,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,6351055,0,5621d1d8,other,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deeplabv3-mobilevit-xx-small,apple,MobileViTForSemanticSegmentation,296,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,1851719,0,535af098,other,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+mobilevit-small,apple,MobileViTForImageClassification,2156,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,5572645,0,14ad46bb,other,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+mobilevit-xx-small,apple,MobileViTForImageClassification,347,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,1270109,0,6ced4e0a,other,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+bart-base,facebook,BartModel,4287565,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,137857028,0,ccd3382a,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+bart-large,facebook,BartModel,523031,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,404206966,0,cb0751ce,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+contriever-msmarco,facebook,BertModel,640510,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,109112174,0,d59172a2,-,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+contriever,facebook,BertModel,11989,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,109112174,0,d59172a2,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-base-224,facebook,ConvNextForImageClassification,1195,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,88591654,0,7ab00a65,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-base-384,facebook,ConvNextForImageClassification,503,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,88591654,0,7ab00a65,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-large-224-22k-1k,facebook,ConvNextForImageClassification,532,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,197767526,0,fb35dbce,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-small-224,facebook,ConvNextForImageClassification,1084,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,50223878,0,87bede4e,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-tiny-224,facebook,ConvNextForImageClassification,7627,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,28589228,0,753bc122,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-xlarge-224-22k,facebook,ConvNextForImageClassification,950,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,392900367,0,8bc87977,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+convnext-xlarge-384-22k-1k,facebook,ConvNextForImageClassification,1487,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,350197158,0,b07800d5,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+data2vec-vision-base-ft1k,facebook,Data2VecVisionForImageClassification,896,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,92014184,0,69cd45e4,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-base-distilled-patch16-224,facebook,DeiTForImageClassificationWithTeacher,3896,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,87338303,0,d5e17c06,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-base-distilled-patch16-384,facebook,DeiTForImageClassificationWithTeacher,1089,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,87630143,0,d5e17c06,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-base-patch16-224,facebook,ViTForImageClassification,1627,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86567765,0,8fa842d1,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-base-patch16-384,facebook,ViTForImageClassification,249,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86859605,0,8fa842d1,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-small-distilled-patch16-224,facebook,DeiTForImageClassificationWithTeacher,4774,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,22436543,0,39d02956,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-small-patch16-224,facebook,ViTForImageClassification,2221,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,22050773,0,75dcf183,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-tiny-distilled-patch16-224,facebook,DeiTForImageClassificationWithTeacher,554,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,5910911,0,a22960fb,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+deit-tiny-patch16-224,facebook,ViTForImageClassification,1605,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,5717525,0,4f7bba18,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+dino-vitb16,facebook,ViTModel,5486,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86389357,0,993623dd,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+dino-vitb8,facebook,ViTModel,631,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86398573,0,e9f1512a,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+dino-vits16,facebook,ViTModel,352,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,21813613,0,257fd398,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+dino-vits8,facebook,ViTModel,291,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,21818221,0,825fd897,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+flava-full,facebook,FlavaModel,5282,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,239843835,0,f54edd4f,bsd-3-clause,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+levit-128S,facebook,LevitForImageClassificationWithTeacher,1379,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,75ce3c61,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+maskformer-swin-base-ade,facebook,MaskFormerForInstanceSegmentation,915,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,119679086,0,435797ea,apache-2.0,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+maskformer-swin-base-coco,facebook,MaskFormerForInstanceSegmentation,2485,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,119679086,0,435797ea,apache-2.0,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+maskformer-swin-small-coco,facebook,MaskFormerForInstanceSegmentation,644,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,119679086,0,435797ea,apache-2.0,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+maskformer-swin-tiny-ade,facebook,MaskFormerForInstanceSegmentation,957,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,119679086,0,435797ea,apache-2.0,Image Segmentation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+mbart-large-50,facebook,MBartForConditionalGeneration,750716,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,865117055,0,cc870534,mit,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+opt-125m,facebook,OPTForCausalLM,228909,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,163848370,0,6cd79533,other,Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+opt-350m,facebook,OPTForCausalLM,108185,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,356887800,0,ad0ef94a,other,Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+regnet-y-040,facebook,RegNetForImageClassification,694,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,20615520,0,e61a4c01,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-mae-base,facebook,ViTMAEForPreTraining,11994,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,e6e74056,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-mae-large,facebook,ViTMAEForPreTraining,5655,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,affe8660,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+xlm-roberta-xl,facebook,XLMRobertaXLForMaskedLM,958,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,4125012789,0,24c40de1,mit,Fill-Mask,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+bert2bert L-24 wmt de en,google,BertGenerationEncoder,1524,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,335040717,0,d49341c1,apache-2.0,Translation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+byt5-base,google,T5ForConditionalGeneration,3256,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,581780174,0,e9c73447,apache-2.0,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+byt5-large,google,T5ForConditionalGeneration,780,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,1228479606,0,1ca21db0,apache-2.0,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+byt5-small,google,T5ForConditionalGeneration,41266,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,299685500,0,2.83E+14,apache-2.0,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+canine-c,google,CanineModel,1775,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,51c875ff,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+canine-s,google,CanineModel,10734,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,51c875ff,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+ddpm-celebahq-256,google,UNet2DModel,1827,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,113662494,0,a5e0de9e,apache-2.0,Unconditional Image Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+ddpm-cifar10-32,google,UNet2DModel,1945,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,35742306,0,31e11b2b,apache-2.0,Unconditional Image Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+electra-base-discriminator,google,ElectraForPreTraining,179212,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,109105394,0,8a65da14,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+electra-base-generator,google,ElectraForMaskedLM,30181,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,56802220,0,55ef183d,apache-2.0,Fill-Mask,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+electra-large-discriminator,google,ElectraForPreTraining,46237,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,334639574,0,b3e531eb,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+electra-small-discriminator,google,ElectraForPreTraining,446832,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,13486322,0,70bef88d,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+fnet-base,google,FNetForMaskedLM,178925,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,ce0cff8a,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+mobilebert-uncased,google,MobileBertForMaskedLM,48600,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,40247413,0,4295f30f,apache-2.0,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+owlvit-base-patch16,google,OwlViTForObjectDetection,2261,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,2a2d9322,apache-2.0,Object Detection,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+owlvit-base-patch32,google,OwlViTForObjectDetection,10221,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,302ff610,apache-2.0,Object Detection,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+owlvit-large-patch14,google,OwlViTForObjectDetection,2642,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,-,0,2565922f,apache-2.0,Object Detection,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+t5-small-ssm-nq,google,Linear,2505,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,196608,0,920c0322,apache-2.0,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-base-patch16-224-in21k,google,ViTModel,614852,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86389357,0,993623dd,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-base-patch16-224,google,ViTForImageClassification,1305984,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86567765,0,8fa842d1,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-base-patch16-384,google,ViTForImageClassification,7771,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86859605,0,8fa842d1,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-base-patch32-224-in21k,google,ViTModel,3348,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,88045933,0,307dc71a,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-base-patch32-384,google,ViTForImageClassification,1806,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,88297301,0,da31f94d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-huge-patch14-224-in21k,google,ViTModel,927,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,632404749,0,e6073acb,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-large-patch16-224-in21k,google,ViTModel,642,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304351437,0,afcb2f64,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-large-patch16-224,google,ViTForImageClassification,607,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304326837,0,62c9365b,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-large-patch16-384,google,ViTForImageClassification,684,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304715957,0,62c9365b,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-large-patch32-224-in21k,google,ViTModel,882,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86389357,0,993623dd,apache-2.0,Feature Extraction,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+vit-large-patch32-384,google,ViTForImageClassification,3062,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,306632885,0,05fbb6ac,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-base-patch16-224-pt22k-ft22k,microsoft,BeitForImageClassification,13214,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,108040913,0,17293472,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-base-patch16-224-pt22k,microsoft,BeitForMaskedImageModeling,1999,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,92422044,0,76e338ee,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-base-patch16-224,microsoft,BeitForImageClassification,4097,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,92014184,0,cd2ea289,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-base-patch16-384,microsoft,BeitForImageClassification,2193,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,134367464,0,cd2ea289,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-large-patch16-224-pt22k-ft22k,microsoft,BeitForImageClassification,384,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,340414369,0,16db572d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-large-patch16-224-pt22k,microsoft,BeitForMaskedImageModeling,542,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,312142432,0,de648727,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-large-patch16-384,microsoft,BeitForImageClassification,252,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,431994424,0,b7efd875,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+beit-large-patch16-512,microsoft,BeitForImageClassification,2832,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,707589688,0,b7efd875,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+codebert-base-mlm,microsoft,RobertaForMaskedLM,273375,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,163311822,0,bb3e7c3b,-,Fill-Mask,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+cvt-13,microsoft,CvtForImageClassification,7775,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,19984994,0,7d8bd070,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+prophetnet-large-uncased,microsoft,ProphetNetForConditionalGeneration,5629,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,421677051,0,dd2215e4,-,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+resnet-101,microsoft,ResNetForImageClassification,303,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,44496488,0,c25a8655,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+resnet-152,microsoft,ResNetForImageClassification,303,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,60117096,0,432f1b45,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+resnet-18,microsoft,ResNetForImageClassification,677,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,11684712,0,4fa34148,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+resnet-34,microsoft,ResNetForImageClassification,288,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,21789160,0,34b5e579,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+resnet-50,microsoft,ResNetForImageClassification,113970,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,25530472,0,649b58e4,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-base-patch4-window12-384-in22k,microsoft,SwinForImageClassification,1546,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,119270870,0,00040b7f,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-base-patch4-window12-384,microsoft,SwinForImageClassification,381,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,97908845,0,4ae8ed0d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-base-patch4-window7-224-in22k,microsoft,SwinForImageClassification,6434,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,110250050,0,00040b7f,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-base-patch4-window7-224,microsoft,SwinForImageClassification,1783,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,88888025,0,4ae8ed0d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-large-patch4-window12-384-in22k,microsoft,SwinForImageClassification,26264,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,242572310,0,c296f66d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-large-patch4-window7-224-in22k,microsoft,SwinForImageClassification,244,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,230104510,0,c296f66d,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-large-patch4-window7-224,microsoft,SwinForImageClassification,8406,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,198071893,0,cb300b56,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-small-patch4-window7-224,microsoft,SwinForImageClassification,562,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,50516251,0,90e0ffd2,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swin-tiny-patch4-window7-224,microsoft,SwinForImageClassification,7898,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,28818337,0,d403933e,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+swinv2-tiny-patch4-window8-256,microsoft,SwinForImageClassification,1754,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,28771675,0,d403933e,apache-2.0,Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-base-handwritten,microsoft,ViTModel,6461,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86653549,0,e45f61ed,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-base-printed,microsoft,ViTModel,18133,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,86653549,0,e45f61ed,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-large-handwritten,microsoft,ViTModel,1876,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304666829,0,4b504cc2,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-large-printed,microsoft,ViTModel,2727,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304666829,0,4b504cc2,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-large-str,microsoft,ViTModel,229,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,304666829,0,4b504cc2,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-small-handwritten,microsoft,DeiTModel,1138,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,21960301,0,5513139b,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+trocr-small-stage1,microsoft,VisionEncoderDecoderModel,585,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,61316403,0,d071f647,-,-,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+xprophetnet-large-wiki100-cased,microsoft,XLMProphetNetForConditionalGeneration,540,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,871333730,0,105cdd91,-,Text2Text Generation,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+clip-vit-base-patch16,openai,CLIPModel,70786,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,149585208,0,5fa6777a,-,Zero-Shot Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+clip-vit-base-patch32,openai,CLIPModel,2330296,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,151241784,0,25380eec,-,Zero-Shot Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,
+clip-vit-large-patch14,openai,CLIPModel,11601851,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,427563136,0,d79341f4,-,Zero-Shot Image Classification,pytorch,-,-,-,-,-,-,-,-,,,,,,,