Feature Extraction
Transformers
Safetensors
Korean
sysone
decision-model
classification
router
korean
custom_code
Instructions to use leapshared/nari-router-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leapshared/nari-router-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leapshared/nari-router-4b", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("leapshared/nari-router-4b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
17.16 다섯째 학습: 말을 실기 받아쓰기 꼴로 맞춰 학습(nari_route.stt_form, 서빙도 같은 함수) · 학습 7,141 · 검증 955 · 개발 505, 씨앗 2 · 2에포크 · lm_weight 0.0 · 온도 1.45(합친 판으로 맞춤), 놓친 멈춤 먼저 고름, 2 GB 조각(저장소 routing.md 17.16절)
d2fdf6d verified Download training_args.json from leapshared/nari-router-4b: direct link, hf CLI and curl.
- Browser
- Download file 509 Bytes
-
https://huggingface.co/leapshared/nari-router-4b/resolve/main/training_args.json
- Command line
-
hf download hf://leapshared/nari-router-4b/training_args.json
-
curl -L -o training_args.json https://huggingface.co/leapshared/nari-router-4b/resolve/main/training_args.json
509 Bytes
| { | |
| "variant": "short_stop", | |
| "lr": 0.0002, | |
| "head_lr": null, | |
| "epochs": 3, | |
| "batch": 8, | |
| "rank": 16, | |
| "alpha": 32.0, | |
| "dropout": 0.05, | |
| "safety_weight": 2.0, | |
| "warmup_ratio": 0.05, | |
| "weight_decay": 0.0, | |
| "grad_clip": 1.0, | |
| "seed": 2, | |
| "grad_checkpointing": true, | |
| "limit": null, | |
| "evals_per_epoch": 2, | |
| "stt": true, | |
| "epoch": 2, | |
| "merged_layers": 248, | |
| "temperature": 1.45, | |
| "lm_weight": 0.0, | |
| "base": "blackmallangcow/nari-decision-4b@029c4628a38859dce5b0db1742e9c97545aabe12", | |
| "temperature_adapter": 1.45 | |
| } |