Add DGX Spark (aarch64/Blackwell) setup fixes and documentation

- utils/vllm_manager.py: drop --disable-log-requests, removed in vLLM 0.26.0
- core/ftpo_trainer.py: pass token_type_ids to the 3 model forward calls in
  compute_loss -- transformers 5.5.0's Gemma3 requires it during training
  for causal-mask construction (Gemma3 is natively multimodal)
- configs/gemma-3-4b-it.yaml: lower vllm_gpu_memory_utilization 0.85->0.5,
  since the DGX Spark's 121GB is unified CPU/GPU memory and the default
  starved the OS, causing swap thrashing
- DGX_SPARK_SETUP.md: full writeup of the above plus the parts that don't
  live in this repo (two-conda-env split to resolve a vllm/unsloth
  transformers version conflict, flash-attn source build flags, torch/CUDA
  version matching, ~/.triton/cache permissions)

The antislop-vllm submodule also needed a one-line fix (removing an invalid
reference_compile kwarg in utils/refusal_detector.py that was silently
disabling refusal filtering) -- documented in DGX_SPARK_SETUP.md rather than
committed as a submodule pointer change, since we don't have push access to
upstream's antislop-vllm repo.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

core/ftpo_trainer.py

@@ -219,6 +219,7 @@ class FTPOTrainer(DPOTrainer):
             ids,
             attention_mask=attn,
             position_ids=pos_full,
+            token_type_ids=torch.zeros_like(ids),
             use_cache=False,
             return_dict=True,
         )
@@ -259,11 +260,13 @@ class FTPOTrainer(DPOTrainer):
                     with self.null_ref_context():
                         ref_logits_last = model(
                             ids, attention_mask=attn, position_ids=pos_full,
+                            token_type_ids=torch.zeros_like(ids),
                             use_cache=False, return_dict=True,
                         ).logits[:, -1, :]
                 else:
                     ref_logits_last = self.ref_model(
                         ids, attention_mask=attn, position_ids=pos_full,
+                        token_type_ids=torch.zeros_like(ids),
                         use_cache=False, return_dict=True,
                     ).logits[:, -1, :]