Instructions to use ndemoss28/Ladder-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ndemoss28/Ladder-3B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "ndemoss28/Ladder-3B") - Notebooks
- Google Colab
- Kaggle
Download run_config.json from ndemoss28/Ladder-3B: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/ndemoss28/Ladder-3B/resolve/main/run_config.json
- Command line
-
hf download hf://ndemoss28/Ladder-3B/run_config.json
-
curl -L -o run_config.json https://huggingface.co/ndemoss28/Ladder-3B/resolve/main/run_config.json
2.04 kB
| { | |
| "name": "ladder-3b-kaggle", | |
| "data": { | |
| "dataset": "open-r1/codeforces-cots", | |
| "config_name": "solutions_py_decontaminated", | |
| "split": "train", | |
| "max_tokens": 8192, | |
| "min_tokens": 256, | |
| "min_rating": null, | |
| "max_rating": null, | |
| "require_finish_reason": "stop", | |
| "dedup_by_problem": true, | |
| "verify_solutions": true, | |
| "verify_max_tests": 4, | |
| "verify_timeout": 5.0, | |
| "verify_memory_limit_mb": 1024, | |
| "verify_workers": 8, | |
| "verify_keep_unverifiable": true, | |
| "val_fraction": 0.04, | |
| "seed": 17, | |
| "max_samples": 0 | |
| }, | |
| "model": { | |
| "base_model": "unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit", | |
| "max_seq_length": 8192, | |
| "load_in_4bit": true, | |
| "lora_r": 32, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.0, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ], | |
| "use_gradient_checkpointing": "unsloth" | |
| }, | |
| "train": { | |
| "output_dir": "/kaggle/working/outputs/ladder-3b-kaggle", | |
| "per_device_train_batch_size": 1, | |
| "gradient_accumulation_steps": 16, | |
| "num_train_epochs": 1.0, | |
| "max_steps": 150, | |
| "learning_rate": 0.0001, | |
| "warmup_ratio": 0.03, | |
| "lr_scheduler_type": "cosine", | |
| "weight_decay": 0.01, | |
| "optim": "adamw_8bit", | |
| "max_grad_norm": 0.5, | |
| "logging_steps": 5, | |
| "save_steps": 75, | |
| "save_total_limit": 2, | |
| "eval_steps": 100, | |
| "seed": 17, | |
| "train_on_completions_only": true, | |
| "hub_model_id": null, | |
| "push_to_hub": false | |
| }, | |
| "eval": { | |
| "dataset": "open-r1/codeforces-cots", | |
| "config_name": "solutions_py_decontaminated", | |
| "split": "train", | |
| "num_problems": 40, | |
| "samples_per_problem": 1, | |
| "problem_types": [ | |
| "diff" | |
| ], | |
| "max_new_tokens": 4096, | |
| "temperature": 0.0, | |
| "top_p": 0.95, | |
| "seed": 17, | |
| "timeout_seconds": 8.0, | |
| "memory_limit_mb": 1024, | |
| "max_tests_per_problem": 12, | |
| "case_sensitive": false, | |
| "results_path": "/kaggle/working/outputs/eval/results.json" | |
| } | |
| } |