Instructions to use RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True) model = AutoModel.from_pretrained("RESMP-DEV/LFM2.5-Encoder-230M-Code-BF16", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "schema_version": 1, | |
| "algorithm": "symmetric_in_batch_infonce", | |
| "source_model": "LiquidAI/LFM2.5-Encoder-230M", | |
| "source_revision": "0b649ad0c684378b03d4d8304f7577a662ab89bc", | |
| "train_sha256": "426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629", | |
| "validation_sha256": "9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096", | |
| "train_rows": 24626, | |
| "source_train_rows": 42626, | |
| "training_language_counts": { | |
| "c": 389, | |
| "cpp": 268, | |
| "cuda": 1, | |
| "go": 3000, | |
| "java": 3000, | |
| "javascript": 3000, | |
| "php": 3000, | |
| "python": 3000, | |
| "ruby": 3000, | |
| "rust": 2980, | |
| "shell": 2, | |
| "toml": 21, | |
| "typescript": 2945, | |
| "yaml": 20 | |
| }, | |
| "validation_rows": 5319, | |
| "configuration": { | |
| "model": "LiquidAI/LFM2.5-Encoder-230M", | |
| "revision": "0b649ad0c684378b03d4d8304f7577a662ab89bc", | |
| "train": "/home/ubuntu/corpus-v1/train.jsonl", | |
| "validation": "/home/ubuntu/corpus-v1/validation.jsonl", | |
| "output": "/home/ubuntu/LFM2.5-Encoder-230M-Code-BF16", | |
| "epochs": 2, | |
| "batch_size": 32, | |
| "gradient_accumulation": 1, | |
| "eval_batch_size": 64, | |
| "query_length": 96, | |
| "passage_length": 384, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.1, | |
| "warmup_ratio": 0.1, | |
| "temperature": 0.05, | |
| "validation_limit": 0, | |
| "train_limit": 0, | |
| "rows_per_language": 3000, | |
| "seed": 17 | |
| }, | |
| "initial_validation": { | |
| "pairs": 5319, | |
| "mrr": 0.13434511350191802, | |
| "recall_at_1": 0.10434292160180485, | |
| "recall_at_5": 0.15811242714796014, | |
| "recall_at_10": 0.18518518518518517 | |
| }, | |
| "history": [ | |
| { | |
| "pairs": 5319, | |
| "mrr": 0.5052553469703251, | |
| "recall_at_1": 0.43090806542583193, | |
| "recall_at_5": 0.5877044557247603, | |
| "recall_at_10": 0.640721940214326, | |
| "epoch": 1, | |
| "train_loss": 1.1061554163965663 | |
| }, | |
| { | |
| "pairs": 5319, | |
| "mrr": 0.5139807531816127, | |
| "recall_at_1": 0.44030832863320174, | |
| "recall_at_5": 0.5961646926113932, | |
| "recall_at_10": 0.6480541455160744, | |
| "epoch": 2, | |
| "train_loss": 0.5155125687041735 | |
| } | |
| ], | |
| "best_validation_mrr": 0.5139807531816127, | |
| "elapsed_seconds": 764.2277336, | |
| "cuda": { | |
| "device": "NVIDIA RTX A6000", | |
| "torch": "2.13.0+cu130", | |
| "peak_memory_bytes": 2742739456 | |
| } | |
| } | |