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The city of Abeokuta is in Mozambique.
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The city of Abeokuta is in Nigeria.
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The city of Abidjan is in China.
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The city of Abidjan is in Côte d'Ivoire.
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The city of Abobo is in Côte d'Ivoire.
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The city of Abobo is in India.
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[ 60, 61, 62, 63, 64, 65, 66, 67, 68, 5 ]
The city of Abu Dhabi is in Oman.
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The city of Abu Dhabi is in the United Arab Emirates.
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The city of Abu Ghurayb is in Indonesia.
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The city of Abu Ghurayb is in Iraq.
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[ 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 9 ]
The city of Abuja is in China.
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The city of Abuja is in Nigeria.
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The city of Acapulco de Juarez is in Mexico.
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The city of Acapulco de Juarez is in Morocco.
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The city of Accra is in China.
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The city of Accra is in Ghana.
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The city of Adana is in Togo.
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The city of Adana is in Turkey.
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The city of Addis Ababa is in Ethiopia.
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The city of Addis Ababa is in Indonesia.
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The city of Aden is in Japan.
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The city of Aden is in Yemen.
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The city of Agadir is in Bangladesh.
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The city of Agadir is in Morocco.
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The city of Aguascalientes is in China.
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The city of Aguascalientes is in Mexico.
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The city of Ahmedabad is in China.
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The city of Ahmedabad is in India.
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The city of Ahvaz is in China.
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28
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The city of Ahvaz is in Iran.
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The city of Ajmer is in India.
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The city of Ajmer is in Russia.
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The city of Aktobe is in Afghanistan.
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The city of Aktobe is in Kazakhstan.
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The city of Al Ahmadi is in Japan.
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The city of Al Ahmadi is in Kuwait.
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The city of Al Ain City is in India.
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The city of Al Ain City is in the United Arab Emirates.
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The city of Al Basrah al Qadimah is in China.
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The city of Al Basrah al Qadimah is in Iraq.
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The city of Al Fayyum is in Chile.
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The city of Al Fayyum is in Egypt.
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The city of Al Hudaydah is in South Korea.
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The city of Al Hudaydah is in Yemen.
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The city of Al Mahallah al Kubra is in Afghanistan.
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The city of Al Mahallah al Kubra is in Egypt.
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The city of Al Mawsil al Jadidah is in India.
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The city of Al Mawsil al Jadidah is in Iraq.
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The city of Albuquerque is in Egypt.
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The city of Albuquerque is in the United States.
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The city of Aleppo is in China.
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The city of Aleppo is in Syria.
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The city of Algiers is in Algeria.
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The city of Algiers is in China.
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The city of Aligarh is in China.
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The city of Aligarh is in India.
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The city of Almaty is in Iraq.
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The city of Almaty is in Kazakhstan.
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The city of Amman is in Jordan.
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The city of Amman is in Malaysia.
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The city of Amravati is in China.
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The city of Amravati is in India.
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The city of Amritsar is in India.
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The city of Amritsar is in Moldova.
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The city of Ankara is in Japan.
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The city of Ankara is in Turkey.
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The city of Anqing is in Brazil.
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66
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The city of Anqing is in China.
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67
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The city of Ansan-si is in Canada.
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The city of Ansan-si is in South Korea.
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The city of Anshan is in Cameroon.
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70
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The city of Anshan is in China.
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The city of Anshun is in Brazil.
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The city of Anshun is in China.
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The city of Antalya is in Germany.
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The city of Antalya is in Turkey.
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The city of Antananarivo is in Germany.
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The city of Antananarivo is in Madagascar.
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The city of Antwerpen is in Belgium.
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78
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The city of Antwerpen is in Japan.
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The city of Anyang is in China.
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The city of Anyang is in Kuwait.
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The city of Anyang-si is in Iraq.
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The city of Anyang-si is in South Korea.
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The city of Aparecida de Goiania is in Brazil.
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84
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The city of Aparecida de Goiania is in China.
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85
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The city of Ar Raqqah is in Iraq.
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86
cities
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The city of Ar Raqqah is in Syria.
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87
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[ 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 87 ]
The city of Aracaju is in Brazil.
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88
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The city of Aracaju is in Russia.
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cities
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[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 89 ]
The city of Arak is in India.
0
10
0
90
90
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 944, 945, 946, 947, 948, 949, 950, 951, 952, 90 ]
The city of Arak is in Iran.
1
10
0
91
91
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 953, 954, 955, 956, 957, 958, 959, 960, 961, 91 ]
The city of Arequipa is in India.
0
11
0
92
92
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 92 ]
The city of Arequipa is in Peru.
1
11
0
93
93
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 93 ]
The city of Asansol is in China.
0
11
0
94
94
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 94 ]
The city of Asansol is in India.
1
11
0
95
95
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 95 ]
The city of Ashgabat is in Bangladesh.
0
12
0
96
96
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 96 ]
The city of Ashgabat is in Turkmenistan.
1
14
0
97
97
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 97 ]
The city of Asmara is in Eritrea.
1
13
0
98
98
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 98 ]
The city of Asmara is in Indonesia.
0
11
0
99
99
cities
raw
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 ]
[ 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 99 ]
End of preview. Expand in Data Studio

meta-llama/Llama-3.1-70B — Activation Dataset

Cached activations extracted from meta-llama/Llama-3.1-70B (revision 349b2ddb53ce8f2849a6c168a81980ab25258dac).

Geometry of Truth curated dataset activations for Llama 3.1 70B base

Contents

Tensor Layers Dim Pooling Shards Row Bytes
hidden_layers 0-79 8192 - 4 -
  • Prompts: 7660
  • Format version: 2.0

Load with lmprobe

from lmprobe import load_activations, Probe

acts = load_activations("alliedtoasters/got-activations-llama3.1-70b-base", layers=[0])
probe = Probe(classifier="logistic_regression", random_state=42)
probe.fit_from_activations(acts[0], labels)

Load without lmprobe (standalone)

import json
import pyarrow.parquet as pq
from safetensors import safe_open

# Load the index — all metadata is embedded in the Parquet schema
table = pq.read_table("index/train-00000-of-00001.parquet")
df = table.to_pandas()
meta = json.loads(table.schema.metadata[b"lmprobe:tensors"])

# Get layer 0 activation for prompt 0
row = df.iloc[0]
pattern = meta["hidden_layers"]["file_pattern"]
path = pattern.format(layer=0, shard=row["shard_index"])
with safe_open(path, framework="pt") as f:
    vec = f.get_tensor("hidden.layer_0")[row["row_offset"]]
    # vec.shape: (8192,)

Full-sequence dataset: The shard_index / row_offset columns always address the last-token pooled vector. For per-token access, use the token_shard_ids and token_shard_offsets list columns — see the lmprobe:tensors schema metadata for details.

Load with HF Datasets

from datasets import load_dataset

# Shows prompt text + labels in Dataset Viewer
ds = load_dataset("alliedtoasters/got-activations-llama3.1-70b-base")
print(ds["train"][0])  # {"text": "...", "label": ..., ...}

Provenance

  • lmprobe version: 0.9.1
  • Extraction backend: local
  • Created: 2026-04-02T12:10:26.702849+00:00
  • PyTorch: 2.11.0+cu130
  • Transformers: 5.4.0
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