Text Generation
PEFT
Safetensors
English
lora
gemma2
mechanistic-interpretability
epistemic-fine-tuning
ai-safety
logos
substrate-persistence
Instructions to use LumenSyntax/logos23-gemma2-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use LumenSyntax/logos23-gemma2-2b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b") model = PeftModel.from_pretrained(base_model, "LumenSyntax/logos23-gemma2-2b") - Notebooks
- Google Colab
- Kaggle
Download training_metadata.json from LumenSyntax/logos23-gemma2-2b: direct link, hf CLI and curl.
- Browser
- Download file 765 Bytes
-
https://huggingface.co/LumenSyntax/logos23-gemma2-2b/resolve/main/training_metadata.json
- Command line
-
hf download hf://LumenSyntax/logos23-gemma2-2b/training_metadata.json
-
curl -L -o training_metadata.json https://huggingface.co/LumenSyntax/logos23-gemma2-2b/resolve/main/training_metadata.json
765 Bytes
| { | |
| "model": "logos23-gemma2_2b", | |
| "family": "gemma2_2b", | |
| "family_name": "Gemma 2 2B", | |
| "base_model": "google/gemma-2-2b", | |
| "base_model_quantized": "google/gemma-2-2b", | |
| "method": "LoRA (bf16)", | |
| "framework": "unsloth", | |
| "lora_rank": 64, | |
| "lora_alpha": 64, | |
| "lora_target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ], | |
| "epochs": 3, | |
| "effective_batch_size": 16, | |
| "load_in_4bit": false, | |
| "learning_rate": 0.0002, | |
| "lr_scheduler": "cosine", | |
| "max_seq_length": 2048, | |
| "dataset": "logos22_nothink.jsonl", | |
| "dataset_size": 895, | |
| "train_on_responses_only": true, | |
| "think_blocks": "stripped (no-think variant)", | |
| "final_loss": 1.2898975720717794, | |
| "runtime_seconds": 221.1209 | |
| } |