ablation-Qwen2.5-1.5B-Instruct-no_reflections-SFT

SkillFactory/ablation-Qwen2.5-1.5B-Instruct-no_reflections-SFT

ablation-Qwen2.5-1.5B-Instruct-no_reflections-SFT is a machine learning model from SkillFactory. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

This model was created as part of the skillfactory-ablationsnoreflectionsreflections5formatsnoreflection experiment using the SkillFactory experiment management system.

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At a glance

Model type: qwen2
Access: Public
Created: Dec 4, 2025
Updated: Dec 4, 2025
SHA: 99777697

Model card

From the Hugging Face model README

M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft

This model was created as part of the skillfactory-ablations__no_reflections_reflections5_formatsno_reflection experiment using the SkillFactory experiment management system.

Model Details

Training Method: LLaMAFactory SFT (Supervised Fine-Tuning)
Stage Name: sft
Experiment: skillfactory-ablations__no_reflections_reflections5_formatsno_reflection

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": "/home/skeh/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__skillfactory_ablations__no_reflections_reflections5_formatsno_reflection", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/datasets/sedrick/skillfactory/temp/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": 1, "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, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__skillfactory-ablations__no_reflections_reflections5_formatsno_reflection__v1", "sf_eval_before_training": false, "sf_wandb_project": "skillfactory-ablations__no_reflections_reflections5_formatsno_reflection_sft", "sf_eval_steps": null, "run_name": "skillfactory-ablations__no_reflections_reflections5_formatsno_reflection_sft"}

Experiment Tracking

🔗 View complete experiment details: Experiment Tracker Dataset

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft")

Type: qwen2
Created: Dec 4, 2025
Updated: Dec 4, 2025