Text Classification
MLX
Safetensors
Laya
Polish
English
devops
decision-model
apple-silicon
experimental
synthetic-data
claude-code
litellm
Instructions to use tebebe/laya-devops-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tebebe/laya-devops-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download tebebe/laya-devops-mlx --local-dir laya-devops-mlx
- Laya
How to use tebebe/laya-devops-mlx 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
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 886 Bytes
aa73ff9 | 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 | {
"encoder": "jhu-clsp/mmBERT-base",
"head_layers": 2,
"max_len": 1024,
"head_max_len": 256,
"max_prefixes": 6,
"act_costs": {
"escalate": 0.5
},
"cost_wrong_act": 3.0,
"amp_dtype": "bf16",
"model_name": "rl-agent",
"temperature": [
0.5,
1.0,
1.0
],
"temperature_by_options": {
"choice:3-5": 0.5
},
"training": {
"description": "Effective V29 sequential fine-tune only; inherited upstream counters omitted.",
"sequential_chunks": 8,
"epochs_per_chunk": 1,
"updates": 23568,
"elapsed_seconds": 40198
},
"fine_tuned": {
"base_model": "local-checkpoint:devops-v29-p07-r1/model",
"dataset_sha256": "df4380ef275ce62aff1ba744b1c418278f8bd18e088126de7aded18d9547cb36",
"best_epoch": 1,
"created": "2026-10-20T12:59:47",
"method": "full",
"objective": "ce",
"tool": "laya-mlx finetune"
}
}
|