Text Generation
PEFT
Safetensors
lora
qlora
behavioral-evaluation
llm
post-training
model-evaluation
qwen
synthetic-data
Instructions to use aamish-ahmad/behaviortune-v1-1-r1-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aamish-ahmad/behaviortune-v1-1-r1-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "aamish-ahmad/behaviortune-v1-1-r1-adapter") - Notebooks
- Google Colab
- Kaggle
| { | |
| "adapter_file_sha256": { | |
| "adapter/README.md": "e38830bd0b1e50cd77eaa5101b7c378da55c6179b89cd3aa1f735dceea723744", | |
| "adapter/adapter_config.json": "d6ebc4b0b2ada4e0020aef4f83d95f7eb4745defe9c4ecacf6d1f3aaca11111f", | |
| "adapter/adapter_model.safetensors": "8d16ef2cb6ff7a982511fd58f21eff52538761f4d198b4cc5cbfd73ca7c9d4de", | |
| "adapter/added_tokens.json": "c0284b582e14987fbd3d5a2cb2bd139084371ed9acbae488829a1c900833c680", | |
| "adapter/merges.txt": "8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5", | |
| "adapter/special_tokens_map.json": "76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd", | |
| "adapter/tokenizer.json": "aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4", | |
| "adapter/tokenizer_config.json": "fed1bd317e1d5c4b32af689a7dec579e51392ba41942ec94a8dfba8ba85deefd", | |
| "adapter/training_args.bin": "ff8df60789de5ccd595bef1bc046475c1d5407ce2df88ea24eaea862c0200064", | |
| "adapter/vocab.json": "ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910" | |
| }, | |
| "data_seed": 147, | |
| "dependency_freeze_sha256": "c15f0bd5445c0b57670f8acfed0500ec6b970b5b20e6c8fe2b26cc3cfbf78486", | |
| "dependency_versions": { | |
| "accelerate": "1.6.0", | |
| "bitsandbytes": "0.45.5", | |
| "datasets": "3.6.0", | |
| "huggingface_hub": "0.30.2", | |
| "peft": "0.15.2", | |
| "safetensors": "0.5.3", | |
| "torch": "2.7.1+cu128", | |
| "transformers": "4.51.3", | |
| "trl": "0.16.1" | |
| }, | |
| "epochs_completed": 3, | |
| "gpu": "NVIDIA A100-SXM4-40GB", | |
| "lora": { | |
| "bias": "none", | |
| "lora_alpha": 64, | |
| "lora_dropout": 0.05, | |
| "r": 32, | |
| "target_modules": "all-linear", | |
| "task_type": "CAUSAL_LM" | |
| }, | |
| "optimizer_steps": 90, | |
| "r1_train_sha256": "be16a7c198cacd727992f4ff82493ec40e5c042f9a485e2091a62eab213f91eb", | |
| "recipe_id": "behaviortune-v1-qlora-primary-r1", | |
| "recipe_sha256": "7e3852e87a59e9a5937e395e6cd2442142c99d2f6cac52576a1662b9e81251eb", | |
| "retry_recipe_used": false, | |
| "schema_version": 1, | |
| "seed": 147, | |
| "status": "PASS", | |
| "train_metrics": { | |
| "total_flos": 1998834477760512.0, | |
| "train_loss": 0.004772673023767501, | |
| "train_runtime": 195.2685, | |
| "train_samples_per_second": 3.687, | |
| "train_steps_per_second": 0.461 | |
| }, | |
| "training": { | |
| "best_checkpoint_selection": false, | |
| "bf16": true, | |
| "data_seed": 147, | |
| "early_stopping": false, | |
| "effective_batch_size": 8, | |
| "epochs": 3, | |
| "final_checkpoint": "epoch_3", | |
| "fp16": false, | |
| "gradient_accumulation_steps": 4, | |
| "gradient_checkpointing": true, | |
| "learning_rate": 0.0002, | |
| "lr_scheduler": "linear", | |
| "max_gradient_norm": 1.0, | |
| "max_sequence_length": 4096, | |
| "optimizer": "adamw_torch", | |
| "packing": false, | |
| "per_device_train_batch_size": 2, | |
| "seed": 147, | |
| "shuffle_train_split": true, | |
| "warmup_ratio": 0.05, | |
| "weight_decay": 0.0 | |
| }, | |
| "training_rows": 240, | |
| "training_run_count": 1 | |
| } | |