Instructions to use sciedoc/neurojenml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sciedoc/neurojenml with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/medgemma-4b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "sciedoc/neurojenml") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "google/medgemma-4b-it", | |
| "base_adapter": "sciedoc/neurojenml", | |
| "adapter_type": "lora", | |
| "lora_config": { | |
| "r": 16, | |
| "alpha": 16, | |
| "dropout": 0, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ] | |
| }, | |
| "training_config": { | |
| "epochs": 3, | |
| "learning_rate": 0.0002, | |
| "batch_size": 2, | |
| "gradient_accumulation": 4, | |
| "warmup_steps": 5, | |
| "fp16": false, | |
| "bf16": false, | |
| "max_seq_length": 2048, | |
| "optimizer": "adamw_8bit", | |
| "weight_decay": 0.01, | |
| "lr_scheduler": "cosine", | |
| "seed": 3407 | |
| }, | |
| "dataset": { | |
| "source": "training_data.jsonl", | |
| "total_examples": 3956, | |
| "train_examples": 1747, | |
| "val_examples": 195 | |
| }, | |
| "results": { | |
| "final_train_loss": 0.0878016290792138, | |
| "final_eval_loss": 0.059457048773765564, | |
| "training_time_seconds": 8703.843399763107, | |
| "quality_guardrail": "response_quality_score", | |
| "training_time_minutes": 145.1 | |
| }, | |
| "hardware": { | |
| "gpu": "Tesla T4", | |
| "gpu_count": 2 | |
| }, | |
| "hf_repo": "https://huggingface.co/sciedoc/neurojenml", | |
| "hf_pushed": true, | |
| "created_at": "2026-07-12T14:53:06.814047" | |
| } |