Instructions to use songdj/Lev-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use songdj/Lev-3B with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Nanbeige/Nanbeige4.2-3B-Base") model = PeftModel.from_pretrained(base_model, "songdj/Lev-3B") - Notebooks
- Google Colab
- Kaggle
File size: 1,619 Bytes
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"status": "cpu-package-verified",
"gpu_parity": "pending",
"model_inference_run": false,
"dataset_examples_read": false,
"seed": 0,
"depths": 2,
"head_loaded_with": "torch.load(weights_only=True, map_location='cpu')",
"head_tensors": {
"q.weight": {
"shape": [
256,
3072
],
"dtype": "torch.float32",
"exact_equal": true
},
"q.bias": {
"shape": [
256
],
"dtype": "torch.float32",
"exact_equal": true
},
"k.weight": {
"shape": [
256,
3072
],
"dtype": "torch.float32",
"exact_equal": true
},
"k.bias": {
"shape": [
256
],
"dtype": "torch.float32",
"exact_equal": true
}
},
"head_schema": [
"base",
"base_revision",
"head",
"head_dim",
"holdout",
"loop",
"lora",
"option_isolation",
"special_embeddings",
"temperature",
"weights",
"weights_dtype"
],
"inference_metadata_exactly_preserved": true,
"metadata_loader": "kev.checkpoint.Checkpoint: passed",
"pointer_head_strict_load": "passed",
"adapter_byte_identical": true,
"adapter_tensor_count": 308,
"base_revision_pinned_in_head_and_adapter": true,
"tokenizer_payload_byte_identical": true,
"private_metadata_and_credential_scan": "passed on metadata, cards, configuration and tensor names",
"remaining_gates": [
"Fresh-environment GPU inference parity on approved development examples",
"Compatible code release validation",
"Final public-release review and explicit visibility change"
]
}
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