Instructions to use macmacmacmac/Sev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use macmacmacmac/Sev-4B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download provenance.json from macmacmacmac/Sev-4B: direct link, hf CLI and curl.
- Browser
- Download file 4.23 kB
-
https://huggingface.co/macmacmacmac/Sev-4B/resolve/main/provenance.json
- Command line
-
hf download hf://macmacmacmac/Sev-4B/provenance.json
-
curl -L -o provenance.json https://huggingface.co/macmacmacmac/Sev-4B/resolve/main/provenance.json
4.23 kB
| { | |
| "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 | |
| } | |
| } | |