初始化项目，由ModelHub XC社区提供模型

Model: danish-foundation-models/dfm-decoder-open-v0-7b-pt
Source: Original Platform

plots/create_plots.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.offsetbox import AnnotationBbox, OffsetImage
+from PIL import Image
+
+#### --- Plot data and logos --- ####
+
+data_danish = {
+    "comma-v0.1-2t": {"parameters (billions)": 7.0, "Danish Performance": 24.2},
+    "Stage 1": {"parameters (billions)": 7.0, "Danish Performance": 35.6},
+    "Stage 2": {"parameters (billions)": 7.0, "Danish Performance": 37.0},
+    # stage 3:
+    "dfm-decoder-open-v0-7b-pt": {
+        "parameters (billions)": 7.0,
+        "Danish Performance": 37.4,
+    },
+    "Pleias-350M": {"parameters (billions)": 0.35, "Danish Performance": 10.4},
+    "Pleias-1.2B": {"parameters (billions)": 1.2, "Danish Performance": 17.7},
+}
+
+data_english = {
+    "comma-v0.1-2t": {"parameters (billions)": 7.0, "English Performance": 51.6},
+    "Stage 1": {"parameters (billions)": 7.0, "English Performance": 48.2},
+    "Stage 2": {"parameters (billions)": 7.0, "English Performance": 50.2},
+    # stage 3:
+    "dfm-decoder-open-v0-7b-pt": {
+        "parameters (billions)": 7.0,
+        "English Performance": 50.1,
+    },
+    "Pleias-350M": {"parameters (billions)": 0.35, "English Performance": 18.9},
+    "Pleias-1.2B": {"parameters (billions)": 1.2, "English Performance": 29.4},
+}
+
+data = {}
+for model in data_danish.keys():
+    data[model] = {
+        "parameters (billions)": data_danish[model]["parameters (billions)"],
+        "Danish Performance": data_danish[model]["Danish Performance"],
+        "English Performance": data_english[model]["English Performance"],
+        "Danish x English Performance": (
+            data_danish[model]["Danish Performance"]
+            + data_english[model]["English Performance"]
+        )
+        / 2,
+    }
+
+
+# Map models to logo files and sizes
+logos = {
+    "comma-v0.1-2t": {"path": "eleutherai.png", "zoom": 0.035},
+    "dfm-decoder-open-v0-7b-pt": {"path": "dfm.png", "zoom": 0.018},
+    "Pleias-350M": {"path": "pleias.png", "zoom": 0.30},
+    "Pleias-1.2B": {"path": "pleias.png", "zoom": 0.30},
+}
+
+
+#### --- Create plot of Danish performance --- ####
+
+model_names = list(data.keys())
+sizes = [data[m]["parameters (billions)"] for m in model_names]
+performance = [data[m]["Danish Performance"] for m in model_names]
+
+# Create plot
+plt.figure(figsize=(7, 7))
+
+# Draw Pareto frontier
+x_curve = np.linspace(0.0, 8, 100)
+y_curve = 19 + 3.9 * np.log(x_curve)
+plt.plot(x_curve, y_curve, "--", color="grey", linewidth=0.7, alpha=0.7)
+plt.text(
+    2,
+    20,
+    "Previous Pareto frontier for openly licensed data",
+    fontsize=8,
+    color="grey",
+    style="italic",
+    rotation=7.5,
+)
+
+
+# Get axis reference
+ax = plt.gca()
+
+# Add logos for specific models
+for i, name in enumerate(model_names):
+    if name in logos:
+        img = Image.open(logos[name]["path"])
+        imagebox = OffsetImage(img, zoom=logos[name]["zoom"])
+        ab = AnnotationBbox(imagebox, (sizes[i], performance[i]), frameon=False, pad=0)
+        ax.add_artist(ab)
+
+        # Add label below/beside logo
+        plt.annotate(
+            name,
+            (sizes[i], performance[i]),
+            xytext=(5, -15),
+            textcoords="offset points",
+            fontsize=9,
+        )
+
+plt.xlabel("Parameters (billions)", fontsize=12)
+plt.ylabel("Danish Performance", fontsize=12)
+
+# Remove top and right spines
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+plt.tight_layout()
+plt.xlim(0, 10)
+plt.ylim(0, 40)
+plt.savefig("danish-perf.png", dpi=300)
+
+
+#### --- Create plot of Danish x English performance --- ####
+
+model_names = list(data.keys())
+sizes = [data[m]["parameters (billions)"] for m in model_names]
+performance = [data[m]["Danish x English Performance"] for m in model_names]
+
+# Create plot
+plt.figure(figsize=(7, 7))
+
+# Draw Pareto frontier
+x_curve = np.linspace(0.2, 8, 100)
+y_curve = 26 + 7 * np.log(x_curve)
+plt.plot(x_curve, y_curve, "--", color="grey", linewidth=0.7, alpha=0.7)
+
+# Get axis reference
+ax = plt.gca()
+
+# Add logos for specific models
+for i, name in enumerate(model_names):
+    if name in logos:
+        img = Image.open(logos[name]["path"])
+        imagebox = OffsetImage(img, zoom=logos[name]["zoom"])
+        ab = AnnotationBbox(imagebox, (sizes[i], performance[i]), frameon=False, pad=0)
+        ax.add_artist(ab)
+
+        # Add label below/beside logo
+        plt.annotate(
+            name,
+            (sizes[i], performance[i]),
+            xytext=(5, -15),
+            textcoords="offset points",
+            fontsize=9,
+        )
+
+plt.xlabel("Parameters (billions)", fontsize=12)
+plt.ylabel("Danish x English Performance", fontsize=12)
+
+# Remove top and right spines
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+plt.tight_layout()
+plt.xlim(0, 10)
+plt.ylim(0, 45)
+plt.savefig("danish-english-perf.png", dpi=300)
+
+
+#### --- Create plot of English performance --- ####
+
+model_names = list(data.keys())
+sizes = [data[m]["parameters (billions)"] for m in model_names]
+performance = [data[m]["English Performance"] for m in model_names]
+
+# Create plot
+plt.figure(figsize=(7, 7))
+
+
+# Get axis reference
+ax = plt.gca()
+
+# Add logos for specific models
+for i, name in enumerate(model_names):
+    if name in logos:
+        img = Image.open(logos[name]["path"])
+        imagebox = OffsetImage(img, zoom=logos[name]["zoom"])
+        ab = AnnotationBbox(imagebox, (sizes[i], performance[i]), frameon=False, pad=0)
+        ax.add_artist(ab)
+
+        # Add label below/beside logo
+        plt.annotate(
+            name,
+            (sizes[i], performance[i]),
+            xytext=(5, -15),
+            textcoords="offset points",
+            fontsize=9,
+        )
+
+plt.xlabel("Parameters (billions)", fontsize=12)
+plt.ylabel("English Performance", fontsize=12)
+
+# Remove top and right spines
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+plt.tight_layout()
+plt.xlim(0, 10)
+plt.ylim(0, 55)
+plt.savefig("english-perf.png", dpi=300)

plots/danish-english-perf.png

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+oid sha256:33857ffd61ab9e36a145ce0aabec24daf7cdcb289658993ece53c3c9dbfe3faa
+size 124889

plots/danish-perf.png

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+size 168354