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volkancirik
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Model Details
Model Provider
volkancirik
Model Tree
poolside/Laguna-XS.2
Input Modalities
Output Modalities
Supported Functionality
| 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.
Laguna XS.2 has launch-day support in vLLM (>= 0.21.0). Serve the base model with this adapter loaded as a LoRA module:
bash
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 lagunaNote: 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.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom 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.
poolside/Laguna-XS.2nm0otq2i6zkmwk6xxo91zcg5pip 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 lagunaimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom 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")