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Harden export paths from review

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- install_llm_compressor: fall back to uv pip when this interpreter has no
  pip seeded (uv-created/relocatable venvs), instead of failing with
  No module named pip.
- LoRA GGUF export: if convert_lora_to_gguf.py is missing (a prebuilt or
  reused CWD llama.cpp install carries binaries but not the converter
  script), force a dedicated source checkout that ships it.
- push_to_hub_gguf(save_method=lora): return on non-main ranks, matching the
  local save_pretrained_gguf lora branch, so only rank 0 converts/uploads.
- compressed export VLM detection: require a vision_config or a
  ForVisionText2Text architecture; a bare *ForConditionalGeneration also
  matches text seq2seq models (T5/BART/Whisper) and is no longer treated as
  a VLM on its own.
- GGUF GPU smoke test: drop SFTConfig(max_length=1024), which raises under
  newer TRL padding-free training; length enforcement is not needed here.
This commit is contained in:
Daniel Han 2026-06-28 09:40:19 +00:00
commit e60f984e2a
2 changed files with 39 additions and 11 deletions
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4
tests/saving/test_gguf_export_and_inference.py
View file

@ -147,7 +147,9 @@ def exported_gguf(tmp_path_factory):
processing_class = tokenizer,
train_dataset = dataset,
args = SFTConfig(
max_length = 1024,
# max_length is left unset: newer TRL enables padding-free training (without packing)
# by default, where SFTConfig(max_length=...) raises because length is not enforced.
max_length = None,
dataset_text_field = "text",
per_device_train_batch_size = 4,
max_steps = 80,

46
unsloth/save.py
View file

@ -1332,7 +1332,20 @@ def install_llm_compressor():
except Exception:
pass
cmd = [sys.executable, "-m", "pip", "install", "llmcompressor"]
# Prefer pip, but fall back to uv when this interpreter has no pip seeded (common in
# uv-created / relocatable venvs), so the export does not hard-fail with "No module named pip".
import importlib.util
if importlib.util.find_spec("pip") is not None:
cmd = [sys.executable, "-m", "pip", "install", "llmcompressor"]
elif shutil.which("uv") is not None:
cmd = ["uv", "pip", "install", "--python", sys.executable, "llmcompressor"]
else:
raise RuntimeError(
"Unsloth: cannot install llm-compressor because this environment has neither pip nor "
f"uv. Install it manually with:\n uv pip install --python {sys.executable} llmcompressor\n"
"(pin torch and transformers to your current versions to avoid upgrading them)."
)
cpath = None
if constraints:
with tempfile.NamedTemporaryFile("w", suffix = ".txt", delete = False) as f:
@ -1344,7 +1357,8 @@ def install_llm_compressor():
except subprocess.CalledProcessError as e:
raise RuntimeError(
"Unsloth: Failed to install llm-compressor. Install it manually with:\n"
f" {sys.executable} -m pip install llmcompressor\n"
f" uv pip install --python {sys.executable} llmcompressor\n"
f"or, if pip is available:\n {sys.executable} -m pip install llmcompressor\n"
"(pin torch and transformers to your current versions to avoid upgrading them).\n"
f"Underlying error: {e}"
)
@ -2774,6 +2788,8 @@ def unsloth_push_to_hub_gguf(
# save_method="lora" exports the adapter itself as a GGUF LoRA (not a merged model).
if save_method is not None and str(save_method).lower() == "lora":
if not is_main_process:
return None # only the main rank converts and uploads, like the local lora branch
_qm = quantization_method
if isinstance(_qm, (list, tuple)) and len(_qm) == 1:
_qm = _qm[0] # the gguf API allows a list; unwrap a single outtype
@ -3146,10 +3162,18 @@ def _unsloth_save_lora_gguf(
# Ensure a full llama.cpp checkout (ships convert_lora_to_gguf.py) and locate the converter.
install_llama_cpp(just_clone_repo = True)
converter = os.path.join(LLAMA_CPP_DEFAULT_DIR, "convert_lora_to_gguf.py")
if not os.path.exists(converter):
# A prebuilt llama.cpp install (or a reused CWD copy) carries binaries but not the
# converter script, so force a dedicated source checkout that ships it.
source_dir = os.path.join(
os.path.dirname(os.path.normpath(LLAMA_CPP_DEFAULT_DIR)), "llama.cpp-source"
)
install_llama_cpp(llama_cpp_folder = source_dir, just_clone_repo = True)
converter = os.path.join(source_dir, "convert_lora_to_gguf.py")
if not os.path.exists(converter):
raise RuntimeError(
f"Unsloth: convert_lora_to_gguf.py not found in '{LLAMA_CPP_DEFAULT_DIR}'. "
"A full llama.cpp checkout is required for LoRA GGUF export."
"Unsloth: convert_lora_to_gguf.py not found after installing a llama.cpp source "
"checkout. A full llama.cpp source checkout is required for LoRA GGUF export."
)
out_gguf = os.path.join(lora_dir, f"{model_name}-lora-{outtype}.gguf")
@ -3938,14 +3962,16 @@ def _unsloth_save_compressed_tensors(
)
unsloth_generic_save(**merge_args)
# 4) Detect VLM + trust_remote_code from the in-memory model config.
# 4) Detect VLM + trust_remote_code from the in-memory model config. A vision/multimodal
# model exposes a vision_config or an explicitly vision-named architecture; a bare
# *ForConditionalGeneration also matches text seq2seq models (T5/BART/Whisper), so it
# is not treated as a VLM on its own.
is_vlm = False
if hasattr(model, "config") and hasattr(model.config, "architectures"):
is_vlm = any(
x.endswith(("ForConditionalGeneration", "ForVisionText2Text"))
for x in (model.config.architectures or [])
if hasattr(model, "config"):
archs = getattr(model.config, "architectures", None) or []
is_vlm = hasattr(model.config, "vision_config") or any(
x.endswith("ForVisionText2Text") for x in archs
)
is_vlm = is_vlm or hasattr(model.config, "vision_config")
if is_vlm:
logger.warning(
"Unsloth: FP8/FP4 compressed export for vision / multimodal models is "