Feature Extraction
Transformers
Safetensors
modernbert
typed-decisions
decision-index
decision-model
cross-encoder
text-embeddings-inference
Instructions to use tasksource/tasksource-decider-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/tasksource-decider-nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tasksource/tasksource-decider-nano")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tasksource/tasksource-decider-nano") model = AutoModel.from_pretrained("tasksource/tasksource-decider-nano", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 751 Bytes
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"format": "pair-v1",
"arch": "joint",
"base": "cross-encoder/ettin-reranker-150m-v1",
"train_max_length": 2048,
"inference_max_length": 8192,
"step": 48000,
"train": {
"arch": "joint",
"opt_pool": "marker",
"shuffle_options": false,
"manifest": "runs/manifests/mix_all_480k.jsonl",
"extra_manifest": null,
"extra_frac": 0.0,
"lr": 2e-05,
"batch_requests": 8,
"max_k": 32,
"seed": 7,
"total_steps": 48000,
"max_length": 2048,
"pack": 0,
"init": null,
"init_backbone": null,
"gc_tokens": 0,
"layout": "shared",
"pool": "opt",
"row_tokens": 8192,
"tok_budget": 12288
},
"layout": "shared",
"pool": "opt",
"attn": "cached",
"weights_dtype": "bf16"
} |