diff --git a/timesfm-forecasting/examples/global-temperature/output/forecast_output.csv b/timesfm-forecasting/examples/global-temperature/output/forecast_output.csv
index c24104c..0a4de7d 100644
--- a/timesfm-forecasting/examples/global-temperature/output/forecast_output.csv
+++ b/timesfm-forecasting/examples/global-temperature/output/forecast_output.csv
@@ -1,13 +1,13 @@
-date,point_forecast,q10,q20,q30,q40,q50,q60,q70,q80,q90,q99
-2025-01-01,1.2593384,1.248188,1.140702,1.1880752,1.2137158,1.2394564,1.2593384,1.2767732,1.297132,1.32396,1.367888
-2025-02-01,1.2856668,1.2773758,1.1406044,1.1960833,1.2322671,1.2593892,1.2856668,1.3110137,1.3400218,1.3751202,1.4253658
-2025-03-01,1.2950127,1.2869918,1.126852,1.1876173,1.234988,1.2675052,1.2950127,1.328448,1.354729,1.4035482,1.4642649
-2025-04-01,1.2207624,1.2084007,1.0352504,1.1041918,1.151865,1.1853008,1.2207624,1.256663,1.2898555,1.3310349,1.4016538
-2025-05-01,1.1702554,1.153313,0.9691495,1.0431063,1.0932612,1.1276176,1.1702554,1.201966,1.2390311,1.2891905,1.3632389
-2025-06-01,1.1455553,1.1275499,0.94203794,1.0110554,1.0658777,1.1061188,1.1455553,1.1806211,1.2180579,1.2702757,1.345366
-2025-07-01,1.1702348,1.1510556,0.9503718,1.0347577,1.0847733,1.1287677,1.1702348,1.2114835,1.2482276,1.2997853,1.3807325
-2025-08-01,1.2026825,1.1859496,0.9709255,1.0594383,1.1106675,1.1579902,1.2026825,1.2399211,1.2842004,1.3408126,1.419526
-2025-09-01,1.1909748,1.1784849,0.95943713,1.0403702,1.103606,1.1511956,1.1909748,1.2390201,1.2832941,1.3354731,1.416972
-2025-10-01,1.1490841,1.1264795,0.9079477,0.99529266,1.0548235,1.1052223,1.1490841,1.1897774,1.240414,1.2868769,1.3775467
-2025-11-01,1.0804785,1.0624356,0.8361266,0.9259792,0.9882403,1.0386353,1.0804785,1.1281581,1.1759715,1.228377,1.3122478
-2025-12-01,1.0613453,1.0366092,0.80220693,0.89521873,0.9593707,1.0152239,1.0613453,1.1032857,1.15315,1.216908,1.2959521
+date,point_forecast,mean,q10,q20,q30,q40,q50,q60,q70,q80,q90
+2025-01-01,1.2223774,1.2215943,1.1230627,1.1613995,1.1832488,1.2030286,1.2223774,1.240989,1.2626991,1.2927711,1.339603
+2025-02-01,1.2563584,1.2502017,1.1482248,1.1891649,1.2134029,1.2355918,1.2563584,1.2787917,1.3061507,1.3358022,1.3880283
+2025-03-01,1.286477,1.2816916,1.1694773,1.2141376,1.2430503,1.2636719,1.286477,1.3101407,1.3365126,1.3725822,1.4265702
+2025-04-01,1.240488,1.2406754,1.119298,1.1689228,1.1957527,1.2162383,1.240488,1.265359,1.2880774,1.3237736,1.3806401
+2025-05-01,1.2026378,1.1969143,1.0776879,1.1280149,1.1554759,1.1801765,1.2026378,1.2274072,1.2546852,1.2890899,1.3469675
+2025-06-01,1.21002,1.1963896,1.0811386,1.1352499,1.1687461,1.1853771,1.21002,1.2333429,1.255994,1.2938039,1.3532466
+2025-07-01,1.2253109,1.2151253,1.0917634,1.1474797,1.176413,1.2029413,1.2253109,1.250956,1.2764856,1.3113455,1.3728349
+2025-08-01,1.2421811,1.2292916,1.1043029,1.1598811,1.1929151,1.2164187,1.2421811,1.266998,1.2924675,1.3304923,1.394975
+2025-09-01,1.269735,1.2603163,1.1239274,1.1872942,1.2162873,1.242951,1.269735,1.2976494,1.3236798,1.3580028,1.4252181
+2025-10-01,1.2496669,1.2436218,1.0962446,1.1630417,1.1938806,1.221712,1.2496669,1.2757387,1.3041382,1.3376691,1.409816
+2025-11-01,1.2135266,1.2031629,1.0545524,1.1223581,1.157211,1.1864623,1.2135266,1.2447275,1.2706528,1.3091388,1.3801678
+2025-12-01,1.2034141,1.1867243,1.0412307,1.1113251,1.1433371,1.1752325,1.2034141,1.2308054,1.2621957,1.2912495,1.370078
diff --git a/timesfm-forecasting/examples/global-temperature/output/forecast_output.json b/timesfm-forecasting/examples/global-temperature/output/forecast_output.json
index d1bd036..4919e17 100644
--- a/timesfm-forecasting/examples/global-temperature/output/forecast_output.json
+++ b/timesfm-forecasting/examples/global-temperature/output/forecast_output.json
@@ -1,5 +1,5 @@
 {
-  "model": "TimesFM 1.0 (200M) PyTorch",
+  "model": "TimesFM 2.5 (200M) PyTorch",
   "input": {
     "source": "NOAA GISTEMP Global Temperature Anomaly",
     "n_observations": 36,
@@ -23,166 +23,166 @@
       "2025-12"
     ],
     "point": [
-      1.25933837890625,
-      1.285666823387146,
-      1.2950127124786377,
-      1.2207623720169067,
-      1.170255422592163,
-      1.1455552577972412,
-      1.1702347993850708,
-      1.2026824951171875,
-      1.1909748315811157,
-      1.1490840911865234,
-      1.080478549003601,
-      1.0613453388214111
+      1.2223774194717407,
+      1.2563583850860596,
+      1.286476969718933,
+      1.240488052368164,
+      1.202637791633606,
+      1.2100199460983276,
+      1.2253109216690063,
+      1.2421810626983643,
+      1.2697349786758423,
+      1.2496669292449951,
+      1.2135266065597534,
+      1.2034140825271606
     ],
     "quantiles": {
+      "mean": [
+        1.2215943336486816,
+        1.25020170211792,
+        1.281691551208496,
+        1.240675449371338,
+        1.1969143152236938,
+        1.1963895559310913,
+        1.215125322341919,
+        1.229291558265686,
+        1.260316252708435,
+        1.243621826171875,
+        1.2031629085540771,
+        1.186724305152893
+      ],
       "10%": [
-        1.2481880187988281,
-        1.2773758172988892,
-        1.286991834640503,
-        1.2084007263183594,
-        1.1533130407333374,
-        1.1275498867034912,
-        1.1510555744171143,
-        1.1859495639801025,
-        1.1784849166870117,
-        1.1264795064926147,
-        1.0624356269836426,
-        1.036609172821045
+        1.1230627298355103,
+        1.1482248306274414,
+        1.1694773435592651,
+        1.119297981262207,
+        1.0776878595352173,
+        1.0811386108398438,
+        1.0917633771896362,
+        1.1043028831481934,
+        1.123927354812622,
+        1.0962445735931396,
+        1.054552435874939,
+        1.0412306785583496
       ],
       "20%": [
-        1.1407020092010498,
-        1.1406043767929077,
-        1.126852035522461,
-        1.0352504253387451,
-        0.9691494703292847,
-        0.9420379400253296,
-        0.9503718018531799,
-        0.970925509929657,
-        0.9594371318817139,
-        0.9079477190971375,
-        0.8361266255378723,
-        0.8022069334983826
+        1.161399483680725,
+        1.1891648769378662,
+        1.2141375541687012,
+        1.168922781944275,
+        1.1280149221420288,
+        1.1352498531341553,
+        1.1474796533584595,
+        1.1598811149597168,
+        1.1872942447662354,
+        1.1630417108535767,
+        1.1223580837249756,
+        1.1113251447677612
       ],
       "30%": [
-        1.1880751848220825,
-        1.1960833072662354,
-        1.187617301940918,
-        1.104191780090332,
-        1.0431063175201416,
-        1.01105535030365,
-        1.0347577333450317,
-        1.0594383478164673,
-        1.040370225906372,
-        0.9952926635742188,
-        0.9259791970252991,
-        0.8952187299728394
+        1.18324875831604,
+        1.2134028673171997,
+        1.2430503368377686,
+        1.195752739906311,
+        1.1554758548736572,
+        1.1687461137771606,
+        1.1764130592346191,
+        1.1929150819778442,
+        1.2162872552871704,
+        1.193880558013916,
+        1.1572109460830688,
+        1.1433371305465698
       ],
       "40%": [
-        1.2137157917022705,
-        1.232267141342163,
-        1.2349879741668701,
-        1.151865005493164,
-        1.0932612419128418,
-        1.0658776760101318,
-        1.084773302078247,
-        1.1106674671173096,
-        1.1036059856414795,
-        1.0548235177993774,
-        0.9882403016090393,
-        0.9593706727027893
+        1.2030285596847534,
+        1.2355917692184448,
+        1.263671875,
+        1.216238260269165,
+        1.1801764965057373,
+        1.1853771209716797,
+        1.2029412984848022,
+        1.216418743133545,
+        1.2429510354995728,
+        1.2217119932174683,
+        1.1864622831344604,
+        1.1752325296401978
       ],
       "50%": [
-        1.2394564151763916,
-        1.2593891620635986,
-        1.267505168914795,
-        1.1853008270263672,
-        1.127617597579956,
-        1.1061187982559204,
-        1.128767728805542,
-        1.1579902172088623,
-        1.1511956453323364,
-        1.1052223443984985,
-        1.03863525390625,
-        1.0152238607406616
+        1.2223774194717407,
+        1.2563583850860596,
+        1.286476969718933,
+        1.240488052368164,
+        1.202637791633606,
+        1.2100199460983276,
+        1.2253109216690063,
+        1.2421810626983643,
+        1.2697349786758423,
+        1.2496669292449951,
+        1.2135266065597534,
+        1.2034140825271606
       ],
       "60%": [
-        1.25933837890625,
-        1.285666823387146,
-        1.2950127124786377,
-        1.2207623720169067,
-        1.170255422592163,
-        1.1455552577972412,
-        1.1702347993850708,
-        1.2026824951171875,
-        1.1909748315811157,
-        1.1490840911865234,
-        1.080478549003601,
-        1.0613453388214111
+        1.2409889698028564,
+        1.2787916660308838,
+        1.3101407289505005,
+        1.2653590440750122,
+        1.2274072170257568,
+        1.2333428859710693,
+        1.2509560585021973,
+        1.266998052597046,
+        1.2976493835449219,
+        1.2757387161254883,
+        1.2447274923324585,
+        1.2308053970336914
       ],
       "70%": [
-        1.27677321434021,
-        1.3110136985778809,
-        1.3284480571746826,
-        1.2566629648208618,
-        1.2019660472869873,
-        1.1806211471557617,
-        1.2114834785461426,
-        1.2399210929870605,
-        1.2390201091766357,
-        1.1897773742675781,
-        1.1281580924987793,
-        1.1032856702804565
+        1.2626991271972656,
+        1.3061506748199463,
+        1.336512565612793,
+        1.2880773544311523,
+        1.2546851634979248,
+        1.2559939622879028,
+        1.276485562324524,
+        1.292467474937439,
+        1.323679804801941,
+        1.30413818359375,
+        1.2706527709960938,
+        1.2621957063674927
       ],
       "80%": [
-        1.2971320152282715,
-        1.3400218486785889,
-        1.3547290563583374,
-        1.2898554801940918,
-        1.2390310764312744,
-        1.2180578708648682,
-        1.248227596282959,
-        1.2842004299163818,
-        1.2832940816879272,
-        1.240414023399353,
-        1.175971508026123,
-        1.153149962425232
+        1.2927711009979248,
+        1.3358021974563599,
+        1.372582197189331,
+        1.3237736225128174,
+        1.2890899181365967,
+        1.2938039302825928,
+        1.3113454580307007,
+        1.3304922580718994,
+        1.358002781867981,
+        1.3376691341400146,
+        1.3091387748718262,
+        1.2912495136260986
       ],
       "90%": [
-        1.3239599466323853,
-        1.3751201629638672,
-        1.403548240661621,
-        1.3310348987579346,
-        1.2891905307769775,
-        1.2702757120132446,
-        1.2997852563858032,
-        1.3408125638961792,
-        1.3354730606079102,
-        1.286876916885376,
-        1.2283769845962524,
-        1.2169079780578613
-      ],
-      "99%": [
-        1.3678879737854004,
-        1.4253658056259155,
-        1.4642648696899414,
-        1.40165376663208,
-        1.3632389307022095,
-        1.3453660011291504,
-        1.380732536315918,
-        1.4195259809494019,
-        1.416972041130066,
-        1.3775466680526733,
-        1.3122477531433105,
-        1.2959520816802979
+        1.3396029472351074,
+        1.3880282640457153,
+        1.426570177078247,
+        1.3806401491165161,
+        1.3469674587249756,
+        1.3532465696334839,
+        1.3728349208831787,
+        1.394974946975708,
+        1.425218105316162,
+        1.409816026687622,
+        1.380167841911316,
+        1.3700779676437378
       ]
     }
   },
   "summary": {
-    "forecast_mean_c": 1.186,
-    "forecast_max_c": 1.295,
-    "forecast_min_c": 1.061,
-    "vs_last_year_mean": -0.067
+    "forecast_mean_c": 1.235,
+    "forecast_max_c": 1.286,
+    "forecast_min_c": 1.203,
+    "vs_last_year_mean": -0.017
   }
 }
\ No newline at end of file
diff --git a/timesfm-forecasting/examples/global-temperature/output/forecast_visualization.png b/timesfm-forecasting/examples/global-temperature/output/forecast_visualization.png
index 0124703..f965a5a 100644
Binary files a/timesfm-forecasting/examples/global-temperature/output/forecast_visualization.png and b/timesfm-forecasting/examples/global-temperature/output/forecast_visualization.png differ
diff --git a/timesfm-forecasting/examples/global-temperature/run_forecast.py b/timesfm-forecasting/examples/global-temperature/run_forecast.py
index 8e83587..abd8b6e 100644
--- a/timesfm-forecasting/examples/global-temperature/run_forecast.py
+++ b/timesfm-forecasting/examples/global-temperature/run_forecast.py
@@ -11,6 +11,7 @@ from pathlib import Path
 
 import numpy as np
 import pandas as pd
+import timesfm
 
 # Preflight check
 print("=" * 60)
@@ -35,27 +36,28 @@ print(
 # TimesFM expects a list of 1D numpy arrays
 input_series = df["anomaly_c"].values.astype(np.float32)
 
-# Load TimesFM 1.0 (PyTorch)
-# NOTE: TimesFM 2.5 PyTorch checkpoint has a file format issue at time of writing.
-# The model.safetensors file is not loadable via torch.load().
-# Using TimesFM 1.0 PyTorch which works correctly.
-print("\n🤖 Loading TimesFM 1.0 (200M) PyTorch...")
-import timesfm
+# Load TimesFM 2.5 (PyTorch)
+print("\n🤖 Loading TimesFM 2.5 (200M) PyTorch...")
 
-hparams = timesfm.TimesFmHparams(horizon_len=12)
-checkpoint = timesfm.TimesFmCheckpoint(
-    huggingface_repo_id="google/timesfm-1.0-200m-pytorch"
+model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
+    "google/timesfm-2.5-200m-pytorch",
+    torch_compile=False,
 )
-model = timesfm.TimesFm(hparams=hparams, checkpoint=checkpoint)
+model.compile(timesfm.ForecastConfig(
+    max_context=512,
+    max_horizon=12,
+    normalize_inputs=True,
+    use_continuous_quantile_head=True,
+    fix_quantile_crossing=True,
+))
 
 # Forecast
 print("\n📈 Running forecast (12 months ahead)...")
 forecast_input = [input_series]
-frequency_input = [0]  # Monthly data
 
 point_forecast, experimental_quantile_forecast = model.forecast(
-    forecast_input,
-    freq=frequency_input,
+    horizon=12,
+    inputs=forecast_input,
 )
 
 print(f"   Point forecast shape: {point_forecast.shape}")
@@ -65,9 +67,8 @@ print(f"   Quantile forecast shape: {experimental_quantile_forecast.shape}")
 point = point_forecast[0]  # Shape: (horizon,)
 quantiles = experimental_quantile_forecast[0]  # Shape: (horizon, num_quantiles)
 
-# TimesFM quantiles: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99]
-# Index mapping: 0=10%, 1=20%, ..., 4=50% (median), ..., 9=99%
-quantile_labels = ["10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%", "99%"]
+# TimesFM 2.5 columns: 0=mean, 1=10%, 2=20%, ..., 5=50% (median), ..., 9=90%
+quantile_labels = ["mean", "10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%"]
 
 # Create forecast dates (2025 monthly)
 last_date = df["date"].max()
@@ -80,16 +81,16 @@ output_df = pd.DataFrame(
     {
         "date": forecast_dates.strftime("%Y-%m-%d"),
         "point_forecast": point,
-        "q10": quantiles[:, 0],
-        "q20": quantiles[:, 1],
-        "q30": quantiles[:, 2],
-        "q40": quantiles[:, 3],
-        "q50": quantiles[:, 4],  # Median
-        "q60": quantiles[:, 5],
-        "q70": quantiles[:, 6],
-        "q80": quantiles[:, 7],
-        "q90": quantiles[:, 8],
-        "q99": quantiles[:, 9],
+        "mean": quantiles[:, 0],
+        "q10": quantiles[:, 1],
+        "q20": quantiles[:, 2],
+        "q30": quantiles[:, 3],
+        "q40": quantiles[:, 4],
+        "q50": quantiles[:, 5],  # Median
+        "q60": quantiles[:, 6],
+        "q70": quantiles[:, 7],
+        "q80": quantiles[:, 8],
+        "q90": quantiles[:, 9],
     }
 )
 
@@ -100,7 +101,7 @@ output_df.to_csv(output_dir / "forecast_output.csv", index=False)
 
 # JSON output for the report
 output_json = {
-    "model": "TimesFM 1.0 (200M) PyTorch",
+    "model": "TimesFM 2.5 (200M) PyTorch",
     "input": {
         "source": "NOAA GISTEMP Global Temperature Anomaly",
         "n_observations": len(df),
@@ -135,24 +136,24 @@ print("=" * 60)
 print(
     f"\n📅 Forecast period: {forecast_dates[0].strftime('%Y-%m')} to {forecast_dates[-1].strftime('%Y-%m')}"
 )
-print(f"\n🌡️  Temperature Anomaly Forecast (°C above 1951-1980 baseline):")
-print(f"\n   {'Month':<10} {'Point':>8} {'80% CI':>15} {'90% CI':>15}")
+print("\n🌡️  Temperature Anomaly Forecast (°C above 1951-1980 baseline):")
+print(f"\n   {'Month':<10} {'Point':>8} {'60% CI':>15} {'80% CI':>15}")
 print(f"   {'-' * 10} {'-' * 8} {'-' * 15} {'-' * 15}")
-for i, (date, pt, q10, q90, q05, q95) in enumerate(
+for i, (date, pt, q20, q80, q10, q90) in enumerate(
     zip(
         forecast_dates.strftime("%Y-%m"),
         point,
-        quantiles[:, 1],  # 20%
-        quantiles[:, 7],  # 80%
-        quantiles[:, 0],  # 10%
-        quantiles[:, 8],  # 90%
+        quantiles[:, 2],  # 20%
+        quantiles[:, 8],  # 80%
+        quantiles[:, 1],  # 10%
+        quantiles[:, 9],  # 90%
     )
 ):
     print(
-        f"   {date:<10} {pt:>8.3f} [{q10:>6.3f}, {q90:>6.3f}] [{q05:>6.3f}, {q95:>6.3f}]"
+        f"   {date:<10} {pt:>8.3f} [{q20:>6.3f}, {q80:>6.3f}] [{q10:>6.3f}, {q90:>6.3f}]"
     )
 
-print(f"\n📊 Summary Statistics:")
+print("\n📊 Summary Statistics:")
 print(f"   Mean forecast:  {point.mean():.3f}°C")
 print(
     f"   Max forecast:   {point.max():.3f}°C (Month: {forecast_dates[point.argmax()].strftime('%Y-%m')})"
@@ -162,6 +163,6 @@ print(
 )
 print(f"   vs 2024 mean:   {point.mean() - df['anomaly_c'].iloc[-12:].mean():+.3f}°C")
 
-print(f"\n✅ Output saved to:")
+print("\n✅ Output saved to:")
 print(f"   {output_dir / 'forecast_output.csv'}")
 print(f"   {output_dir / 'forecast_output.json'}")
diff --git a/timesfm-forecasting/examples/global-temperature/visualize_forecast.py b/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
index 86e7d72..500badc 100644
--- a/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
+++ b/timesfm-forecasting/examples/global-temperature/visualize_forecast.py
@@ -16,6 +16,8 @@ from __future__ import annotations
 import json
 from pathlib import Path
 
+import matplotlib
+matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -57,11 +59,11 @@ def main() -> None:
         label="Historical (NOAA GISTEMP)",
     )
 
-    # Plot 90% CI (outer band)
-    ax.fill_between(dates, q10, q90, alpha=0.2, color="#dc2626", label="90% CI")
+    # Plot 80% CI (outer band)
+    ax.fill_between(dates, q10, q90, alpha=0.2, color="#dc2626", label="80% CI")
 
-    # Plot 80% CI (inner band)
-    ax.fill_between(dates, q20, q80, alpha=0.3, color="#dc2626", label="80% CI")
+    # Plot 60% CI (inner band)
+    ax.fill_between(dates, q20, q80, alpha=0.3, color="#dc2626", label="60% CI")
 
     # Plot point forecast
     ax.plot(