Instructions to use JacoDuToit/steer-full_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JacoDuToit/steer-full_7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "JacoDuToit/steer-full_7b") - Notebooks
- Google Colab
- Kaggle
| { | |
| "run": "full_7b", | |
| "started_at": "2026-07-23T18:26:39+0000", | |
| "argv": [ | |
| "scripts/run.py", | |
| "configs/full_7b.yaml" | |
| ], | |
| "stages": [ | |
| "vectors", | |
| "data", | |
| "eval_m0", | |
| "train", | |
| "eval_m1" | |
| ], | |
| "config_path": "configs/full_7b.yaml", | |
| "config": { | |
| "model_id": "Qwen/Qwen2.5-7B-Instruct", | |
| "device": "auto", | |
| "dtype": "auto", | |
| "seed": 0, | |
| "layer": 14, | |
| "train_alphas": [ | |
| 0.4, | |
| 0.8, | |
| 1.2 | |
| ], | |
| "eval_alphas": [ | |
| 0.4, | |
| 0.8, | |
| 1.0, | |
| 1.2, | |
| 1.6 | |
| ], | |
| "efficacy_alpha": 0.8, | |
| "efficacy_min_rate": 0.3, | |
| "n_contrast_pairs": 10, | |
| "qa_source": "json", | |
| "concepts_path": "data/concepts_paper.json", | |
| "qa_path": "data/qa_open.json", | |
| "relevant_frac": 0.6, | |
| "steered_frac": 0.7, | |
| "eval_question_frac": 0.2, | |
| "repeats_per_question": 6, | |
| "alpaca_replay_frac": 0.5, | |
| "eval_concepts_per_split": 40, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "lora_targets": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ], | |
| "lr": 0.0001, | |
| "epochs": 1, | |
| "effective_batch_size": 16, | |
| "train_batch_size": 2, | |
| "max_seq_len": 256, | |
| "max_new_tokens": 64, | |
| "eval_batch_size": 4, | |
| "bootstrap_resamples": 10000, | |
| "capability_tasks": { | |
| "mmlu": 5, | |
| "gsm8k_cot": 8 | |
| }, | |
| "capability_apply_chat_template": true, | |
| "capability_limit": "mmlu=15,gsm8k_cot=200", | |
| "wandb_project": null, | |
| "wandb_entity": null, | |
| "hub_repo_id": "JacoDuToit/steer-full_7b", | |
| "hub_private": true, | |
| "vectors_path": "results/full_7b/vectors.pt", | |
| "train_examples_path": "results/full_7b/train_examples.json", | |
| "eval_questions_path": "results/full_7b/eval_questions.json", | |
| "adapter_dir": "results/full_7b/m1_resist_adapter", | |
| "results_dir": "results/full_7b" | |
| }, | |
| "git": { | |
| "commit": null, | |
| "branch": null, | |
| "dirty": null, | |
| "remote": null | |
| }, | |
| "env": { | |
| "python": "3.11.10", | |
| "platform": "Linux-6.8.0-49-generic-x86_64-with-glibc2.35", | |
| "accelerator": "NVIDIA GeForce RTX 3090", | |
| "packages": { | |
| "torch": "2.13.0+cu126", | |
| "transformers": "5.14.1", | |
| "peft": "0.19.1", | |
| "accelerate": "1.14.0", | |
| "datasets": "5.0.0", | |
| "numpy": "2.4.6", | |
| "wandb": null, | |
| "huggingface_hub": "1.24.0" | |
| } | |
| }, | |
| "data": { | |
| "concepts_path": { | |
| "path": "data/concepts_paper.json", | |
| "sha256": "adc6aee9b1cc537af09ca2229ccbb470adfdaa3e73d05be69df031e042c4347b" | |
| }, | |
| "qa_path": { | |
| "path": "data/qa_open.json", | |
| "sha256": "fa2356571420fd8c2a444aee6e8c879b865e0cd21f5ade2938a0308effbed8f2" | |
| } | |
| }, | |
| "status": "success", | |
| "wandb_url": null, | |
| "headline": "clean 100%->100% · steer_heldout@1.6 correct 0%->20%", | |
| "hub_url": "https://huggingface.co/JacoDuToit/steer-full_7b", | |
| "finished_at": "2026-07-23T20:16:36+0000", | |
| "artifacts": { | |
| "eval_m0.jsonl": { | |
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| "bytes": 4749617 | |
| }, | |
| "eval_m1.jsonl": { | |
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| "bytes": 4129347 | |
| }, | |
| "eval_questions.json": { | |
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| "bytes": 30264 | |
| }, | |
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| "bytes": 301292 | |
| }, | |
| "vectors.pt": { | |
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| } |