Text Classification
Transformers
Safetensors
Laya
Chinese
system-one
calibrated-decisions
rlcd
chinese
classification
routing
scoring
guardrails
non-autoregressive
commercial-license-required
Instructions to use ZichenAI/layazh-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZichenAI/layazh-zh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZichenAI/layazh-zh")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZichenAI/layazh-zh", device_map="auto") - Laya
How to use ZichenAI/layazh-zh with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
NovaAI
layazh-zh: Chinese System-1 decision model (Laya recipe, RoBERTa-large encoder + decision head); full-eval acc 0.6951 vs upstream 0.4783
f69ec12 verified Download rl_agent_config.json from ZichenAI/layazh-zh: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/ZichenAI/layazh-zh/resolve/main/rl_agent_config.json
- Command line
-
hf download hf://ZichenAI/layazh-zh/rl_agent_config.json
-
curl -L -o rl_agent_config.json https://huggingface.co/ZichenAI/layazh-zh/resolve/main/rl_agent_config.json
2.06 kB
| { | |
| "encoder": "hfl/chinese-roberta-wwm-ext-large", | |
| "encoder_type": "bert", | |
| "head_layers": 2, | |
| "max_len": 512, | |
| "head_max_len": 192, | |
| "max_prefixes": 6, | |
| "act_costs": { | |
| "escalate": 0.5 | |
| }, | |
| "cost_wrong_act": 3.0, | |
| "amp_dtype": "bf16", | |
| "train_dtype": "fp32", | |
| "temperature": [ | |
| 0.467223, | |
| 1.034508, | |
| 0.970277 | |
| ], | |
| "temperature_by_options": { | |
| "choice:11+": 0.466635, | |
| "choice:2": 0.253066, | |
| "choice:3-5": 0.545042, | |
| "noul:2": 0.970277, | |
| "score:3-5": 1.034508 | |
| }, | |
| "model_name": "layazh", | |
| "training": { | |
| "recipe": { | |
| "head_lr": 0.0003, | |
| "encoder_lr": 2e-05, | |
| "warmup_frac": 0.06, | |
| "scheduler": "linear warmup 6% + cosine", | |
| "unfrozen_encoder_layers": 8, | |
| "option_order_augmentation": true, | |
| "grad_checkpointing": true, | |
| "amp": "fp32 (sm_52/Maxwell has no bf16)", | |
| "batch_tokens": 4096, | |
| "optimizer": "AdamW", | |
| "grad_clip": 1.0 | |
| }, | |
| "effective_steps": "~1250 (stopped near plateau; not the full planned schedule)", | |
| "probe_best": 0.7991, | |
| "probe_acc": { | |
| "choice": 0.7243, | |
| "score": 0.7744, | |
| "noul": 0.9638, | |
| "ALL": 0.7991 | |
| }, | |
| "hardware": "GTX Titan X (Maxwell, sm_52, 12GB), torch 2.7.1+cu128" | |
| }, | |
| "calibration": { | |
| "scheme": "bucket", | |
| "report_ece": 0.2546653607969531, | |
| "fit_n": 3122, | |
| "report_n": 3122, | |
| "by_qtype": [ | |
| 0.467223, | |
| 1.034508, | |
| 0.970277 | |
| ], | |
| "by_bucket": { | |
| "choice:11+": 0.466635, | |
| "choice:2": 0.253066, | |
| "choice:3-5": 0.545042, | |
| "noul:2": 0.970277, | |
| "score:3-5": 1.034508 | |
| }, | |
| "all_schemes_report_ece": { | |
| "identity": 0.3511506951291145, | |
| "qtype": 0.26843774177473434, | |
| "bucket": 0.2546653607969531 | |
| } | |
| }, | |
| "evaluation": { | |
| "full_eval_n": 6244, | |
| "full_eval_acc": 0.6951, | |
| "baselines": { | |
| "random": 0.2959, | |
| "majority_class": 0.4734 | |
| }, | |
| "upstream_laya_multilingual_acc": 0.4783, | |
| "ab_n": 1200 | |
| } | |
| } |