Instructions to use adimyth/compass-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adimyth/compass-lora-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "adimyth/compass-lora-v2") - Notebooks
- Google Colab
- Kaggle
Download training.json from adimyth/compass-lora-v2: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/adimyth/compass-lora-v2/resolve/main/training.json
- Command line
-
hf download hf://adimyth/compass-lora-v2/training.json
-
curl -L -o training.json https://huggingface.co/adimyth/compass-lora-v2/resolve/main/training.json
3.38 kB
| { | |
| "args": { | |
| "train": "dev/splits_v2/train.jsonl", | |
| "select": "dev/splits_v2/selection.jsonl", | |
| "out": "release/lora-v2", | |
| "model_name": "Qwen/Qwen3.5-4B", | |
| "rank": 16, | |
| "alpha": 32, | |
| "lora_dropout": 0.05, | |
| "lr": 0.0001, | |
| "weight_decay": 0.01, | |
| "epochs": 2, | |
| "accum": 8, | |
| "fusion": 0.5, | |
| "ordinal_w": 0.5, | |
| "perm_w": 0.5, | |
| "perm_p": 0.5, | |
| "opaque_p": 0.5, | |
| "eval_every": 400, | |
| "patience": 3, | |
| "max_select": 400, | |
| "seed": 0 | |
| }, | |
| "n_train": 2150, | |
| "n_select": 161, | |
| "best_selection_loss": 0.8873302168802816, | |
| "best_step": 250, | |
| "log": [ | |
| { | |
| "step": 0, | |
| "seen": 0, | |
| "loss": 1.336758746236262, | |
| "acc": 0.7080745341614907, | |
| "by_family": { | |
| "adequacy": "7/10", | |
| "ambiguous": "7/7", | |
| "policy": "11/13", | |
| "probability": "29/60", | |
| "routing": "60/71" | |
| } | |
| }, | |
| { | |
| "step": 50, | |
| "seen": 400, | |
| "loss": 1.0610499146397323, | |
| "acc": 0.8260869565217391, | |
| "by_family": { | |
| "adequacy": "6/10", | |
| "ambiguous": "7/7", | |
| "policy": "8/13", | |
| "probability": "47/60", | |
| "routing": "65/71" | |
| } | |
| }, | |
| { | |
| "step": 100, | |
| "seen": 800, | |
| "loss": 1.031379698107586, | |
| "acc": 0.906832298136646, | |
| "by_family": { | |
| "adequacy": "8/10", | |
| "ambiguous": "7/7", | |
| "policy": "9/13", | |
| "probability": "55/60", | |
| "routing": "67/71" | |
| } | |
| }, | |
| { | |
| "step": 150, | |
| "seen": 1200, | |
| "loss": 0.9625220286051789, | |
| "acc": 0.8757763975155279, | |
| "by_family": { | |
| "adequacy": "6/10", | |
| "ambiguous": "6/7", | |
| "policy": "10/13", | |
| "probability": "52/60", | |
| "routing": "67/71" | |
| } | |
| }, | |
| { | |
| "step": 200, | |
| "seen": 1600, | |
| "loss": 0.9321816444499288, | |
| "acc": 0.9316770186335404, | |
| "by_family": { | |
| "adequacy": "9/10", | |
| "ambiguous": "6/7", | |
| "policy": "10/13", | |
| "probability": "60/60", | |
| "routing": "65/71" | |
| } | |
| }, | |
| { | |
| "step": 250, | |
| "seen": 2000, | |
| "loss": 0.8873302168802816, | |
| "acc": 0.9130434782608695, | |
| "by_family": { | |
| "adequacy": "7/10", | |
| "ambiguous": "6/7", | |
| "policy": "8/13", | |
| "probability": "58/60", | |
| "routing": "68/71" | |
| } | |
| }, | |
| { | |
| "step": 301, | |
| "seen": 2406, | |
| "loss": 0.9988113209218529, | |
| "acc": 0.9130434782608695, | |
| "by_family": { | |
| "adequacy": "6/10", | |
| "ambiguous": "6/7", | |
| "policy": "11/13", | |
| "probability": "58/60", | |
| "routing": "66/71" | |
| } | |
| }, | |
| { | |
| "step": 351, | |
| "seen": 2806, | |
| "loss": 1.0362505128805466, | |
| "acc": 0.9254658385093167, | |
| "by_family": { | |
| "adequacy": "7/10", | |
| "ambiguous": "6/7", | |
| "policy": "10/13", | |
| "probability": "58/60", | |
| "routing": "68/71" | |
| } | |
| }, | |
| { | |
| "step": 401, | |
| "seen": 3206, | |
| "loss": 0.990789322077885, | |
| "acc": 0.9192546583850931, | |
| "by_family": { | |
| "adequacy": "7/10", | |
| "ambiguous": "6/7", | |
| "policy": "9/13", | |
| "probability": "58/60", | |
| "routing": "68/71" | |
| } | |
| } | |
| ], | |
| "targets": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "in_proj_qkv", | |
| "in_proj_z", | |
| "out_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ] | |
| } |