Respect classification head skip list on pre-quantized 4-bit checkpoints (#5027) (#5034)

* Respect classification head skip list on pre-quantized 4-bit checkpoints (#5027)

FastLanguageModel.from_pretrained(..., num_labels=N) crashed with
"NotImplementedError: normal_kernel_cuda not implemented for 'Byte'" on
pre-quantized bnb 4-bit checkpoints (e.g. unsloth/Qwen3-4B-bnb-4bit)
when running on transformers 5.x.

Two pieces were needed to close this out:

1. unsloth_zoo PR: add "score", "classifier", "qa_outputs" to
   SKIP_QUANTIZATION_MODULES so replace_with_bnb_linear leaves task
   heads in the compute dtype.

2. This commit: for pre-quantized checkpoints, transformers reads
   llm_int8_skip_modules from the quantization_config baked into
   config.json and ignores the runtime BitsAndBytesConfig we pass via
   kwargs. Unsloth must merge its skip list into
   model_config.quantization_config.llm_int8_skip_modules before the
   from_pretrained call, or the checkpoint's frozen list
   (e.g. ["lm_head", "multi_modal_projector", "merger",
   "modality_projection"]) wins and the `score` head gets converted to
   Linear4bit with uint8 storage, then _init_weights calls normal_ on
   uint8 and crashes.

Also add a defensive post-load cast on the task head to guard against
any residual path that ends up with a non-floating head dtype.

Verified on transformers 4.57.6 and 5.5.0 with:
- unsloth/Qwen3-4B-bnb-4bit + num_labels=3
- unsloth/Qwen3-4B (non-bnb repo, load_in_4bit=True)
- unsloth/Llama-3.2-1B-Instruct + num_labels=3
- unsloth/ModernBERT-large classifier head (bert_classification notebook)
- Regression: causal LM path unchanged, backbone still 4-bit
- 3-step SFT on num_labels=3 confirms gradient flow and weight updates
  on score.weight

Fixes unslothai/unsloth#5027

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unsloth/models/llama.py

@@ -2405,6 +2405,31 @@ class FastLlamaModel:
                 bnb_4bit_compute_dtype = dtype,
                 llm_int8_skip_modules = llm_int8_skip_modules,
             )
+            # For pre-quantized checkpoints (e.g. unsloth/Qwen3-4B-bnb-4bit),
+            # transformers uses the quantization_config baked into the
+            # checkpoint's config.json and ignores the runtime BitsAndBytesConfig
+            # we pass via kwargs. Merge our skip list into that bundled config
+            # so task heads like `score` (for *ForSequenceClassification) stay
+            # in the compute dtype. See unslothai/unsloth#5027.
+            _ckpt_qcfg = getattr(model_config, "quantization_config", None)
+            if _ckpt_qcfg is not None:
+                if isinstance(_ckpt_qcfg, dict):
+                    _ckpt_skip = list(_ckpt_qcfg.get("llm_int8_skip_modules") or [])
+                    for _m in llm_int8_skip_modules:
+                        if _m not in _ckpt_skip:
+                            _ckpt_skip.append(_m)
+                    _ckpt_qcfg["llm_int8_skip_modules"] = _ckpt_skip
+                else:
+                    _ckpt_skip = list(
+                        getattr(_ckpt_qcfg, "llm_int8_skip_modules", None) or []
+                    )
+                    for _m in llm_int8_skip_modules:
+                        if _m not in _ckpt_skip:
+                            _ckpt_skip.append(_m)
+                    try:
+                        _ckpt_qcfg.llm_int8_skip_modules = _ckpt_skip
+                    except Exception:
+                        pass
 
         # https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/12
         # RoPE Scaling's max_position_embeddings must be updated
@@ -2441,6 +2466,18 @@ class FastLlamaModel:
                 attn_implementation = preferred_attn_impl,
                 **kwargs,
             )
+            # Defensive: make sure the task head ended up in a floating dtype.
+            # The primary protection is SKIP_QUANTIZATION_MODULES plus the skip
+            # list merge above; this guards against a downstream path accidentally
+            # leaving the head in an integer storage. See unslothai/unsloth#5027.
+            for _head_name in ("score", "classifier", "qa_outputs"):
+                _head = getattr(model, _head_name, None)
+                if (
+                    _head is not None
+                    and hasattr(_head, "weight")
+                    and not _head.weight.is_floating_point()
+                ):
+                    _head.to(dtype)
         elif not fast_inference:
             model = AutoModelForCausalLM.from_pretrained(
                 model_name,