Text Classification
PEFT
Safetensors
English
decision-model
calibration
lora
multiple-choice
typesafe
qwen3.5
Eval Results (legacy)
Instructions to use jaredpalmer/kev-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jaredpalmer/kev-4b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B-Base") model = PeftModel.from_pretrained(base_model, "jaredpalmer/kev-4b") - Notebooks
- Google Colab
- Kaggle
Research preview: Qwen3-4B-Base + LoRA r16, decision-v4, lr 5e-5 (trial lowdrift-4b-v4/01-trial-1); dev 0.843, transfer 0.759; locked test 0.852 / 0.794 (one ungated read)
1a0cb0a verified Download provenance.json from jaredpalmer/kev-4b: direct link, hf CLI and curl.
- Browser
- Download file 2.71 kB
-
https://huggingface.co/jaredpalmer/kev-4b/resolve/002ed9fb770d4eae7c52f447c0a64245ce07b7f9/provenance.json
- Command line
-
hf download hf://jaredpalmer/kev-4b@002ed9fb770d4eae7c52f447c0a64245ce07b7f9/provenance.json
-
curl -L -o provenance.json https://huggingface.co/jaredpalmer/kev-4b/resolve/002ed9fb770d4eae7c52f447c0a64245ce07b7f9/provenance.json
2.71 kB
| { | |
| "config": { | |
| "epochs": 2, | |
| "seed": 0, | |
| "lr": 5e-05, | |
| "lora": 16, | |
| "accum": 2, | |
| "batch": 4, | |
| "perm_kl": 0.0, | |
| "perm_frac": 0.3, | |
| "ord_w": 0.0, | |
| "p_none": 0.1, | |
| "p_none_distract": 0.12, | |
| "p_distract": 0.15, | |
| "p_none_pair": 0.25, | |
| "synthetic_repeat": 1, | |
| "public_frac": 1.0, | |
| "base": "Qwen/Qwen3-4B-Base", | |
| "dtype": "bf16", | |
| "checkpointing": 1 | |
| }, | |
| "config_sha256": "5d3deac10148d21541e48b12430160d3e7a79e3b4f61bcbbf98f934a79c5367c", | |
| "suite_sha256": "1b33e566d114f9eafeff55b36c221fadb2a4ae358a1b9cc68006e82c7cfad8f1", | |
| "source_hashes": { | |
| "kev/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", | |
| "kev/api.py": "cdb0602684d798ccd3fc2c86be622f2a8e7bcdeee64d07f95604c7d3fd4701ec", | |
| "kev/autoresearch.py": "ca45a4e2bc91ae241d841e6543bc2715f00054d928668d6c8d24bb6f7cfa3db4", | |
| "kev/benchmark.py": "042747fa86d4f10b3bb7c2a45ad3ff671183f45872bc3ab029e7743364a51a28", | |
| "kev/compare.py": "bd0445f021de59e35c7bff9304e39dd2e1211e3877a453575594ae7b81b0ada4", | |
| "kev/composition.py": "7667e02da107548cd85953da60c9b411f7be1cb8aef01e1eb0301b68f8d4457d", | |
| "kev/contrastive.py": "d6c9a577c9ad598416ed96f7c9037fd7541f1a53b36fd9c22bdae547b554ae87", | |
| "kev/data.py": "4296cea986f53383475d37438aa3ca2ba7cb258fe85a78b534a0aeccd02b9711", | |
| "kev/evaluate.py": "07664e4ac95c76054025e0850785fe5a16cef48a964679bdbab0137fe37147c1", | |
| "kev/experiment.py": "96bf40aa3fc9fcfee845adabf61c232708a7cc7e6c9874c3d2af4a4c6364dae7", | |
| "kev/jev.py": "e0213782359ba2f95adbf045ddaf0a008b08b4162bf6a9b66d4ce55fc91a51cb", | |
| "kev/model.py": "214b43200c6f31b673c31023621bec3ed3f713e26542dd9f483f62a9f4d813b9", | |
| "kev/plot.py": "d689c7dd18cde7f9ea77cb4f55c50ff7da1880b21cb2ecf244348e45842a1382", | |
| "kev/publish.py": "d6d581b0fab520305a46ed980cfeec9fcde80d479d70c95bebe8bd47dd91bece", | |
| "kev/serve.py": "86bcf741a8942c8ab0c49a7af7385af6a3e0163d0a931da3dc4faa8ab4ae50b7", | |
| "kev/study_v3.py": "4cda234f0b92391f82ffb441f89bc142c969705ce223dcb1ae52ea782e0b4b63", | |
| "kev/suite.py": "c4db818f83d7abaf4d8349a5b58f20f70439e04127b0989eb1f831be85578d87", | |
| "kev/train.py": "fb509ed3ecb52dcd4d70759c988b1c1a733995d8c1f6187218020e56784c37f6", | |
| "modal_app.py": "db0f9dc408c88d43e07a8cf4c6673267b9c9d451628f5bd8c437cf4ffb4e081e", | |
| "pyproject.toml": "ce566ee11b68c0cb587bb591943641f403d761d820817693dae7590faacf9888", | |
| "uv.lock": "846fdc64a8bd6279a17a4365f18cd4e92ba9622d50d378aafcb5be6604296138" | |
| }, | |
| "git_commit": "639f756ff930fffbd347f5b6d11c0c754240665c", | |
| "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 | |
| } | |