M-examples_with_all_skills-sft
This model was created as part of the examples_with_all_skills experiment using the SkillFactory experiment management system.
Model Details
- Training Method: LLaMAFactory SFT (Supervised Fine-Tuning)
- Stage Name: sft
- Experiment: examples_with_all_skills
Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/datastor1/mwadhwa/code/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z3_config.json", "dataset": "TAUR_dev__D_SFT_C_cd3arg_Qwen2_5_1_5B_Mixed_all_examples_with_skills", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 16, "output_dir": "/datastor1/mwadhwa/tmp/sf/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 2, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "do_eval": true, "eval_strategy": "steps", "eval_steps": 100, "eval_dataset": "TAUR_dev__D_SFT_C_cd3arg_Qwen2_5_1_5B_Mixed_all_examples_with_skills_validation", "per_device_eval_batch_size": 1, "compute_custom_metrics": true, "report_to": "wandb", "run_name": "examples_with_all_skills"}
Experiment Tracking
🔗 View complete experiment details: Experiment Tracker Dataset
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-examples_with_all_skills-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-examples_with_all_skills-sft")
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