Hugging Face's logo

macmacmacmac
/
Sev-4B

PEFT
Safetensors
English
sev
research
cybersecurity
agent-activity
decision-model
lora
Model card Files
xet
 Community
Sev-4B
File size: 4,226 Bytes
8c19e93
 
7f9f3f5
8c19e93
7f9f3f5
8c19e93
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7f9f3f5
8c19e93
 
 
 
a1824ae
 
 
8c19e93
7f9f3f5
 
8c19e93
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a1824ae
 
8c19e93
 
7f9f3f5
8c19e93
 
 
 
 
 
7f9f3f5
 
 
 
8c19e93
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
{
  "config": {
    "epochs": 5,
    "seed": 4,
    "lr": 1e-05,
    "lora": 16,
    "accum": 2,
    "batch": 4,
    "perm_kl": 0,
    "perm_frac": 0.3,
    "ord_w": 0.0,
    "p_none": 0,
    "p_none_distract": 0,
    "p_distract": 0,
    "p_none_pair": 0,
    "synthetic_repeat": 1,
    "public_frac": 1,
    "head_lr": 0.0,
    "weight_decay": 0.01,
    "anchor_w": 0.0,
    "label_smoothing": 0.0,
    "brier_w": 0.0,
    "focal_gamma": 0.0,
    "base": "Qwen/Qwen3.5-4B-Base",
    "base_revision": "1001bb4d826a52d1f399e183466143f4da7b741b",
    "init_from": "macmacmacmac/Sev-4B@v0.3.0-response-policy-research",
    "lora_targets": "all",
    "head_dim": 256,
    "weights_dtype": "fp32",
    "dtype": "bf16",
    "checkpointing": 1,
    "option_isolation": 0,
    "special_embeddings": 0
  },
  "config_sha256": "31d8cf5af214ae4ed0012d1e4fea9fd73e207209cfce1f1112107e4aad65cc9f",
  "suite_sha256": "40d3a8b3bd416a3754ecc30e9791a9ebb14a26c8f2a4422057f769a2dbce4dee",
  "source_hashes": {
    "kev/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
    "kev/anchors.py": "6963eafdb276db5a6c94d939eae675c761555553b8d0f963ffd803448beaabbb",
    "kev/api.py": "7bffacfb762c626b8bc2f670f350295af5ccb0883dbe90239c7d2f8e5ef56582",
    "kev/autoresearch.py": "f7d7fabb9c0df7065bee3fec4aa4bee7028c5d1565847aefe2d74e3d7d40ed8a",
    "kev/benchmark.py": "8d486659a9036102386b077fcbd197fc0d85ae7752a4711666d6b3a95622b26f",
    "kev/calibrate.py": "c1eff4744fabd349e8abca86777a7aa0cbea44904c8223d3b61fca3d43741519",
    "kev/checkpoint.py": "9f1dbc5d3d7d3050aca4ee34e0578c9ad76b7a2a7467dfc7a4ad65e3c471db59",
    "kev/compare.py": "3606dbf98bb305d66420158498cb837304d2edb8dab01cdaf2ec42964bef3435",
    "kev/composition.py": "f335ed17e18e0a544893db5e22b9a059e6ce1b2e14dbb863ac7d7bbf8f3e0536",
    "kev/contrastive.py": "cbb979aa5d40265ad0e64695f94b281d91751fa405811ddfa8212fede111edcf",
    "kev/data.py": "a5a5e200f56b60e4bf30a195a4cc87a74afddae15999ec48c7543a4f9b59f71c",
    "kev/device.py": "d1677fd98ec0979c7284546306e34e0d09298ca4042fc39eb2b32a74f4e975c4",
    "kev/evaluate.py": "999264f2837dcfbf2ec93601aa4745e701698ab850a43ad89324672a6604965c",
    "kev/experiment.py": "1a5ca47d2b3d236e53c87e8c4197704f162c6692aa008521d31ea491945693f0",
    "kev/jev.py": "acd4cc3f1844e438cc83a8d409c15ef78a5ef64d39ea10f583d75a6c7646b243",
    "kev/metrics.py": "db0930d470c0ce16f16dcaf726d49a7a103973a5296cefc5259d1e68707d882b",
    "kev/mlx_model.py": "f582428796faf6bf962b772a69a6227ac3259ccbd3215c0c8b55ce28dbbd0909",
    "kev/model.py": "eac0f56c807b85b5c4077483d922e6bb03f07890de2835efd9372cf20cd01b8e",
    "kev/plot.py": "0874bcff2885d8155a1cceade0de8a275a7163d3c3fd6aa0c294e8ccf5ff7e02",
    "kev/predictors.py": "b2a66dd9f0f0a8026bc94603383bacfdd622e75d1c935173997175606626a9b3",
    "kev/publish.py": "8abc9bcf4a01365697b05cf3c5ad0013462bd92d005f5616954e40ab1100c7aa",
    "kev/serve.py": "c210b1f4598e64b49c399f73603ed372f4314c165ceda50e1340c0466ec1c735",
    "kev/study_v3.py": "7891150aa623e4479c7c789d4dc4662f186132b37b7bac1e1f072b9e08ee13e3",
    "kev/suite.py": "c5e5e27f2fed871b547a021d180b0a7f4a0542a488cce4ee02ad936f433441db",
    "kev/train.py": "a2d85632d73760fb3efe51627ff4fde6af3100ed1c34e75d290c1b00c9899aa9",
    "kev/transfer_v9.py": "0902409742151250a28af1fd8f42258b70fcc161f3deed3ee35fe3f473f3c763",
    "modal_app.py": "639577258f4d581daefa51dad639961280ce92de9da256316ab4124f005a53fb",
    "pyproject.toml": "39ec40f14c88598dee7ac98fb859a5a44e22337235a3064ebd5e89036ce81e30",
    "uv.lock": "a9922dbb89acdef78299fd2b4a8c3f7f0fa1b2bc08b55595b6926fa785a9c466"
  },
  "git_commit": "fcd6756a329f42a515b4b48f83fb8bde9776232f",
  "platform": "Linux-4.19.0-gvisor-x86_64-with-glibc2.36",
  "torch": "2.8.0+cu128",
  "device": "cuda",
  "gpu": "NVIDIA H100 80GB HBM3",
  "legacy_checkpoint": false,
  "measured_checkpoint": {
    "requested": "/runs/sev-html-contrast-4b-v1/00-trial-0/checkpoint",
    "resolved": "/runs/sev-html-contrast-4b-v1/00-trial-0/checkpoint",
    "head_sha256": "2724bebcc54d4846194130594b98d3e3b9c632935c0f1d5077e46e23c48b2dc8",
    "adapter_sha256": "b58b3963175e6c40bd022db5893ebefb6a6f1e1bd1ec11efc6692a089e1dcf51",
    "inference_temperature": 1.0
  }
}