Text Generation
PEFT
Safetensors
English
lora
data-to-text
text-to-data
factual-consistency
hallucination-detection
Instructions to use Loria-MosAIk/xqdt-e2e-llama3.2-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Loria-MosAIk/xqdt-e2e-llama3.2-3b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Loria-MosAIk/xqdt-e2e-llama3.2-3b") - Notebooks
- Google Colab
- Kaggle
Download training_manifest.json from Loria-MosAIk/xqdt-e2e-llama3.2-3b: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/Loria-MosAIk/xqdt-e2e-llama3.2-3b/resolve/main/training_manifest.json
- Command line
-
hf download hf://Loria-MosAIk/xqdt-e2e-llama3.2-3b/training_manifest.json
-
curl -L -o training_manifest.json https://huggingface.co/Loria-MosAIk/xqdt-e2e-llama3.2-3b/resolve/main/training_manifest.json
1.42 kB
| { | |
| "adapter_model_sha256": "e84a82e9347f3cf1b46b1d2768b7a76433f8b2d5ee0d572d1a446a57d040adeb", | |
| "base_model": "meta-llama/Llama-3.2-3B-Instruct", | |
| "dataset_release_label": "e2e", | |
| "hyperparameters": { | |
| "bf16": true, | |
| "dataset": [ | |
| "triple_error_train" | |
| ], | |
| "dataset_num_proc": 1, | |
| "eval_steps": 279, | |
| "fp16": false, | |
| "gradient_accumulation_steps": 4, | |
| "gradient_checkpointing": true, | |
| "learning_rate": 5e-05, | |
| "logging_steps": 93, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "lora_rank": 16, | |
| "max_length": 1024, | |
| "max_new_tokens": 64, | |
| "model": "meta-llama/Llama-3.2-3B-Instruct", | |
| "model_type": "llama3_2", | |
| "num_train_epochs": 16, | |
| "per_device_eval_batch_size": 4, | |
| "per_device_train_batch_size": 4, | |
| "save_steps": 279, | |
| "save_total_limit": 3, | |
| "seed": 2023, | |
| "system": "Identify extra, missing and incorrect triples precisely.", | |
| "target_modules": [ | |
| "all-linear" | |
| ], | |
| "template": "llama3_2", | |
| "torch_dtype": "bfloat16", | |
| "train_type": "lora", | |
| "warmup_ratio": 0.0625, | |
| "weight_decay": 0.01 | |
| }, | |
| "repository": "xqdt-e2e-llama3.2-3b", | |
| "schema_version": 1, | |
| "source_checkpoint_relative_path": "checkpoints/checkpoints_e2e19/output_llama3_3b_e2e19/v0-20260214-225224/checkpoint-6975", | |
| "source_commit": "739c84d868f9d32bcf5c150bdf05c56260cfcaf4", | |
| "training_scope": "joint WebNLG and E2E synthetic data" | |
| } | |