Stage: ParamΔ instruct graft (no training)
Lineage: Base → CPT (asterion) → ParamΔ (this model)
Method: ParamΔ [paramdelta], an instance of task arithmetic [taskarith]: the official post-training delta (Instruct − Base) is summed onto the CPT'd weights (λ=1.0, 149 tensors), transferring instruction-following while keeping domain knowledge. Element-wise merge in fp32, saved bf16; tokenizer + chat template + generation config from the Instruct model. Same recipe validated on the lfm2.5-1.2b-noval round.
Domain: fictional — Orbital Mining Corporation (OMC) technical docs + Mars Express telemetry.

Training


Method	weight arithmetic only — NO gradient training
λ (instruct delta scale)	1.0
Precision	merge fp32 → save bf16

Evaluation

Metric	Value	Note
PPL Asterion held-out	2.12	CPT parent: 1.91
PPL Mars telemetry	1.38	CPT parent: 1.30
PPL general (FineWeb-Edu)	9.13	CPT parent: 7.31

Eval is a held-out, deterministic verifiable harness (synthetic tasks); baseline = the pre-SFT ParamΔ model. See noval-corp/scripts/eval_agentic.py.

Intended use & limitations

Chat / instruction-following over the Asterion domain at zero post-training cost. Baseline for the agentic SFT v2 round.

Limitations:

ParamΔ is validated in the literature on 8B–70B; 1.2B transfer measured, not assumed (headroom regression above is mild and expected).
λ=1.0 not swept.
NOT agentic: does not execute tool-calling trajectories (see -agentic).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("atenareply/lfm2.5-1.2b-asterion-instruct-paramdelta")
model = AutoModelForCausalLM.from_pretrained("atenareply/lfm2.5-1.2b-asterion-instruct-paramdelta")
msgs = [{"role": "user", "content": "What does the Orbital Mining Corporation do?"}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
print(tok.decode(model.generate(**inputs, max_new_tokens=256)[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Citations

ParamΔ — Cao et al., ParamΔ for Direct Weight Mixing, ICLR 2025. arXiv:2504.21023
Task arithmetic — Ilharco et al., Editing Models with Task Arithmetic, 2023. arXiv:2212.04089

Card generated by noval-corp/scripts/gen_model_cards.py (standardized across the noval-corp model family).