volkancirik/Laguna-XS.2-spatial-grounding-lora

Laguna-XS.2 — Spatial Grounding LoRA

A LoRA adapter for poolside/Laguna-XS.2 (33B-total / 3B-active Mixture-of-Experts), trained with reinforcement learning on PrimeIntellect hosted training against a spatial-grounding environment.

Adapter configuration

Field	Value
peft_type	LORA
task_type	CAUSAL_LM
r	16
lora_alpha	32
lora_dropout	0.0
target_modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, experts
base model	poolside/Laguna-XS.2

The adapter targets attention projections and the MoE expert / MLP projections.

Serving with vLLM

Laguna XS.2 has launch-day support in vLLM (>= 0.21.0). Serve the base model with this adapter loaded as a LoRA module:

pip install 'vllm>=0.21.0'

vllm serve poolside/Laguna-XS.2 \
    --enable-lora \
    --lora-modules spatial=volkancirik/Laguna-XS.2-spatial-grounding-lora \
    --tool-call-parser poolside_v1 \
    --reasoning-parser poolside_v1 \
    --enable-auto-tool-choice \
    --max-lora-rank 16 \
    --served-model-name laguna

Note: LoRA over MoE expert layers is not supported by every serving stack. If vLLM rejects the expert-targeted modules, merge the adapter into the base weights first (PeftModel.merge_and_unload() on a GPU/large-RAM host) and serve the merged checkpoint.

Then query the OpenAI-compatible endpoint (http://localhost:8000/v1/chat/completions), passing spatial as the model name.

Loading with PEFT / Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "poolside/Laguna-XS.2"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "volkancirik/Laguna-XS.2-spatial-grounding-lora")

Requires transformers >= 5.7.0 (Laguna support) and peft.

Training

• Platform: PrimeIntellect hosted RL (prime-rl)
• Base: poolside/Laguna-XS.2
• Domain: spatial grounding (relative-position reasoning over rendered scenes)
• Checkpoint id: nm0otq2i6zkmwk6xxo91zcg5