adapter_model.safetensors: LoRA adapter weights.
adapter_config.json: PEFT LoRA configuration.
tokenizer.json, tokenizer_config.json, special_tokens_map.json: tokenizer files used with the adapter.
trainer_state.json, training_args.bin: training metadata kept for traceability.

Optimizer, scheduler, and RNG checkpoint files are intentionally not included.

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "GSAI-ML/LLaDA-1.5"
adapter_id = "mousezhang/math-llada1.5"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)

Adapter Details

Base model: GSAI-ML/LLaDA-1.5
Adapter type: LoRA via PEFT
Task/domain: math reasoning
PEFT task type: CAUSAL_LM
LoRA rank: 128
LoRA alpha: 128
LoRA dropout: 0.05
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training and Evaluation

This model card does not report benchmark scores. Evaluation results should be treated as not provided unless published separately by the model author.

Limitations

This adapter inherits the limitations and license terms of the base model. It is intended for research and experimental use, and outputs should be checked carefully before use in high-stakes settings.