RuleLoopViT principal rule generator — 19,200 examples
This is the final full-parameter SFT checkpoint of
LiquidAI/LFM2.5-1.2B-Instruct from the RuleLoopViT principal training run.
The model receives 2–4 serialized ARC-AGI demonstration pairs and generates a task-specific rule in the project's fixed five-section schema. Training used 300 ARC-AGI-1 task families with 64 balanced fresh episodes per task, for 19,200 examples. Cross-entropy was applied only to the generated rule target, not to the serialized input pairs.
Training configuration
- Full-parameter fine-tuning
- Constant learning rate:
2e-5 - Warmup: none
- Learning-rate decay: none
- Weight decay:
0.1 - Effective batch size:
8 - Precision:
bfloat16 - Maximum sequence length:
12,288
The checkpoint includes its tokenizer, generation configuration, milestone metadata, run manifest, and final evaluation summary. The final downstream metrics are frozen-GL diagnostics, not standalone ARC solve rates.
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "omrisap/ruleloopvit-lfm2.5-1.2b-principal-sft"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
Experiment provenance
The associated W&B run is
sft-lm-principal-300x64-seed42-v1.