feat: migrate from Granite 3 to Granite 4 hybrid models

[planetf1]

Migrate from Granite 3.x to Granite 4.0 Models

Type of PR

• Bug Fix
• New Feature
• Documentation
• Other

Description

• Link to Issue: Closes feat: Update tests & examples to use granite4 #344

Summary

This PR migrates Mellea from Granite 3.x to Granite 4.0 hybrid models across all backends, tests, and documentation. Note: HuggingFace tests remain on Granite 3.3 due to adapter availability constraints (see below).

Changes

Model Definitions (mellea/backends/model_ids.py)

• Added Granite 4 hybrid model identifiers:
  • IBM_GRANITE_4_HYBRID_MICRO (granite-4.0-h-micro)
  • IBM_GRANITE_4_HYBRID_TINY (granite-4.0-h-tiny)
  • IBM_GRANITE_4_HYBRID_SMALL (granite-4-h-small)
• Restored IBM_GRANITE_4_MICRO_3B with per-backend model selection (Ollama: MICRO, Watsonx: SMALL)
• Marked Granite 3 models as deprecated (kept for backward compatibility)
• Added vision model: IBM_GRANITE_3_3_VISION_2B

Backend Updates

• WatsonxAIBackend: Default model → IBM_GRANITE_4_HYBRID_SMALL
• All other backends: Use Granite 4 hybrid models in tests

Test Updates (19 files)

✅ Migrated to Granite 4:

• test/backends/test_watsonx.py
• test/backends/test_ollama.py
• test/backends/test_litellm_*.py (3 files)
• test/backends/test_vllm*.py (2 files)
• test/stdlib/components/*.py (8 files)
• test/stdlib/requirements/*.py (3 files)

⚠️ Remains on Granite 3.3:

• test/backends/test_huggingface.py - See "HuggingFace Test Exception" below

⚠️ Remains on Granite 3.2:

• test/backends/test_vision_ollama.py - See "Vision Model Exception" below

Documentation Updates

• docs/tutorial.md: Updated all examples to Granite 4
• docs/alora.md: Updated training examples, added note about non-hybrid models for adapter training
• docs/examples/*.py: Updated all example scripts

Test Infrastructure

• Removed 48GB memory markers (Granite 4 micro models require ~16GB)
• Fixed CI memory constraints by using MICRO models for Ollama tests
• Restored per-backend model selection for IBM_GRANITE_4_MICRO_3B (matches upstream pattern)

HuggingFace Test Exception

HuggingFace tests remain on Granite 3.3 due to missing aLoRA adapters for Granite 4.

The HF tests require the requirement_check intrinsic adapter, which is only available for Granite 3.x models in ibm-granite/rag-intrinsics-lib. While ibm-granite/granite-lib-rag-r1.0 has Granite 4 support for RAG intrinsics (answerability, context_relevance, etc.), the core intrinsics needed for tests are not yet available.

Follow-up Issue: #359 tracks migration once Granite 4 adapters are released.

Vision Model Exception

Vision tests remain on granite3.2-vision due to Ollama compatibility issues.

The ibm/granite3.3-vision:2b model causes Ollama server crashes with segmentation fault (null pointer dereference in llama runner). Reverted to granite3.2-vision which works reliably.

Follow-up Issue: #360 documents the crash with full stack traces and debugging information.

Testing

• Tests added to the respective file if code was changed
• New code has 100% coverage if code as added
• Ensure existing tests and github automation passes (a maintainer will kick off the github automation when the rest of the PR is populated)

Local Testing

# Fast tests (skip LLM quality checks)
uv run pytest -m "not qualitative"

# Full test suite
uv run pytest

Test Results: 204 passed, 6 skipped, 69 deselected, 1 xpassed

CI Testing

All tests pass in CI with CICD=1 (skips qualitative markers).

Related Issues

• Closes feat: Update tests & examples to use granite4 #344 (Granite 4 migration)
• Follow-up Migrate HuggingFace Tests to Granite 4 When Adapters Available #359 (HuggingFace Granite 4 adapter migration)
• Follow-up Investigate granite-vision-3.3-2b Ollama Compatibility Issue #360 (Vision model Ollama crash investigation)

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• title ~= ^(fix|feat|docs|style|refactor|perf|test|build|ci|chore|revert|release)(?:\(.+\))?:

[planetf1]

Issue with HuggingFace tests

• I moved to using granite v4. First hybrid, then regular. However the intrinsics repo doesn't have granite4 options yet.
  Checking status of intrinsics/aLora (including with hybrid - extra parameters for mamba).

[planetf1]

Looking at CI failures...

[planetf1]

Rebased onto upstream/main and squashed to single commit (bc476b9).

Resolved conflicts - added pytest markers from upstream while preserving model selections.

Note: docs/examples/aLora/101_example.py has pre-existing bug (broken since Nov 2025 refactor, commit 1229206). Separate fix needed.

[jakelorocco]

Looks good! Lets make sure all the intrinsics / adapters tests still run (let me know if you need help testing those). Those tests won't run during the github cicd, so we will need to run them manually.

[jakelorocco]

Were you able to test if this (and the other) intrinsic / adapter tests still worked? I think in this case at least, there's no requirement_check adapter trained for this model?

[planetf1]

I needed #397 to reliably run the tests in a suitable environment (useful when this is merged).

Temporarily I've cherry-picked the commit here & was able to run all the hugging face tests that are active (examples & tests). This is require reverting some of the tests to the granite3.x models -- as some were dependent on adapters that are not yet available. Issue #359 was already open to track that update.

I've also reverted this change. (don't yet have vllm setup - another todo)

[planetf1]

Will look at this tomorrow as I now have a suitable environment

[planetf1]

Suggestion: If we can agree/merge #397 I will then rebase this PR, resolve conflicts, and rerun the full suite locally + hugging face remotely.

[jakelorocco]

Suggestion: If we can agree/merge #397 I will then rebase this PR, resolve conflicts, and rerun the full suite locally + hugging face remotely.

I've approved it!

[planetf1]

Thanks @jakelorocco for the approval.

Rebased, so will need a new review. I can run tests again if needed (ideally after #416 is merged

[planetf1]

Test failing on:

FAILED test/backends/test_litellm_ollama.py::test_async_avalue - litellm.exceptions.APIConnectionError: litellm.APIConnectionError: Ollama_chatException - {"error":"model 'granite4:micro-h' not found"}
= 5 failed, 177 passed, 103 skipped, 1 xpassed, 8 warnings in 518.46s (0:08:38) =

will investigate tomorrow.

[jakelorocco]

Thanks @jakelorocco for the approval.

Rebased, so will need a new review. I can run tests again if needed (ideally after #416 is merged

Added both to my review list. Will look today or morning of tomorrow. Thank you!

[jakelorocco]

Test failing on:

FAILED test/backends/test_litellm_ollama.py::test_async_avalue - litellm.exceptions.APIConnectionError: litellm.APIConnectionError: Ollama_chatException - {"error":"model 'granite4:micro-h' not found"}
= 5 failed, 177 passed, 103 skipped, 1 xpassed, 8 warnings in 518.46s (0:08:38) =

will investigate tomorrow.

@planetf1, when running ollama through litellm, you have to make sure the model has been pulled first. Litellm won't do that automatically. When I tried it that way, I was able to get the test to run. I'm not certain why it only failed for that test though.

[jakelorocco]

lgtm assuming the remaining test issue is sorted

[planetf1]

Hugging face tests mostly work locally, but I am seeing an exception from one which relates to error handling:

FAILED test/backends/test_huggingface.py::test_error_during_generate_with_lock

AttributeError: 'Exception' object has no attribute 'sequences'

During handling of the above exception, another exception occurred:

    async def test_error_during_generate_with_lock(backend) -> None:
        # ... test setup ...
        
        with pytest.raises(Exception, match="Oops!"):
>           await reg_mot.avalue()

test/backends/test_huggingface.py:530: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
mellea/core/base.py:251: in avalue
    await self.astream()
mellea/core/base.py:338: in astream
    await self._post_process(self)
mellea/backends/huggingface.py:921: in post_processing
    output_complete = mot._meta["hf_output"].sequences[0]
E   AttributeError: 'Exception' object has no attribute 'sequences'

test/backends/test_huggingface.py:530: AssertionError
E       AssertionError: Regex pattern did not match.
E         Expected regex: 'Oops!'
E         Actual message: "'Exception' object has no attribute 'sequences'"

= 1 failed, 35 passed, 3 skipped, 346 deselected, 1 xfailed, 11 warnings in 246.67s (0:04:06) =

This looks like an underlying issue (and across all backends), so I opened up #432 to track

[planetf1]

Here's the full test output:

=== STDOUT ===
============================= test session starts ==============================
platform linux -- Python 3.12.12, pytest-9.0.2, pluggy-1.6.0 -- /u/jonesn/.conda/envs/mellea/bin/python3
cachedir: .pytest_cache
rootdir: /proj/dmfexp/eiger/users/jonesn/mellea-c
configfile: pyproject.toml
testpaths: test, docs
plugins: nbmake-1.5.5, asyncio-1.3.0, Faker-40.1.2, timeout-2.4.0, langsmith-0.6.6, anyio-4.12.1, cov-7.0.0
asyncio: mode=Mode.AUTO, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
timeout: 900.0s
timeout method: signal
timeout func_only: False
collecting ... collected 385 items / 346 deselected / 1 skipped / 39 selected

test/backends/test_huggingface.py::test_adapters PASSED                  [  2%]
test/backends/test_huggingface.py::test_system_prompt PASSED             [  5%]
test/backends/test_huggingface.py::test_constraint_lora_with_requirement PASSED [  7%]
test/backends/test_huggingface.py::test_constraint_lora_override PASSED  [ 10%]
test/backends/test_huggingface.py::test_constraint_lora_override_does_not_override_alora PASSED [ 12%]
test/backends/test_huggingface.py::test_llmaj_req_does_not_use_alora PASSED [ 15%]
test/backends/test_huggingface.py::test_instruct PASSED                  [ 17%]
test/backends/test_huggingface.py::test_multiturn PASSED                 [ 20%]
test/backends/test_huggingface.py::test_chat PASSED                      [ 23%]
test/backends/test_huggingface.py::test_format PASSED                    [ 25%]
test/backends/test_huggingface.py::test_generate_from_raw PASSED         [ 28%]
test/backends/test_huggingface.py::test_generate_from_raw_with_format PASSED [ 30%]
test/backends/test_huggingface.py::test_async_parallel_requests PASSED   [ 33%]
test/backends/test_huggingface.py::test_async_avalue PASSED              [ 35%]
test/backends/test_huggingface.py::test_generate_with_lock PASSED        [ 38%]
test/backends/test_huggingface.py::test_generate_with_lock_does_not_block_when_awaiting_value PASSED [ 41%]
test/backends/test_huggingface.py::test_error_during_generate_with_lock FAILED [ 43%]
test/backends/test_huggingface.py::test_assert_correct_adapters PASSED   [ 46%]
test/backends/test_huggingface_tools.py::test_tool PASSED                [ 48%]
test/stdlib/components/intrinsic/test_rag.py::test_answerability PASSED  [ 51%]
test/stdlib/components/intrinsic/test_rag.py::test_query_rewrite PASSED  [ 53%]
test/stdlib/components/intrinsic/test_rag.py::test_citations PASSED      [ 56%]
test/stdlib/components/intrinsic/test_rag.py::test_context_relevance PASSED [ 58%]
test/stdlib/components/intrinsic/test_rag.py::test_hallucination_detection PASSED [ 61%]
test/stdlib/components/intrinsic/test_rag.py::test_answer_relevance PASSED [ 64%]
test/stdlib/components/intrinsic/test_rag.py::test_answer_relevance_classifier PASSED [ 66%]
test/stdlib/components/intrinsic/test_rag.py::test_query_clarification_positive PASSED [ 69%]
test/stdlib/components/intrinsic/test_rag.py::test_query_clarification_negative PASSED [ 71%]
test/stdlib/test_spans.py::test_lazy_spans PASSED                        [ 74%]
test/stdlib/test_spans.py::test_kv XFAIL (Model safety refusal despi...) [ 76%]
docs/examples/aLora/101_example.py::101_example.py SKIPPED (uncondit...) [ 79%]
docs/examples/intrinsics/answer_relevance.py::answer_relevance.py PASSED [ 82%]
docs/examples/intrinsics/answerability.py::answerability.py PASSED       [ 84%]
docs/examples/intrinsics/citations.py::citations.py PASSED               [ 87%]
docs/examples/intrinsics/context_relevance.py::context_relevance.py PASSED [ 89%]
docs/examples/intrinsics/hallucination_detection.py::hallucination_detection.py PASSED [ 92%]
docs/examples/intrinsics/intrinsics.py::intrinsics.py PASSED             [ 94%]
docs/examples/intrinsics/query_rewrite.py::query_rewrite.py PASSED       [ 97%]
docs/examples/mify/rich_document_advanced.py::rich_document_advanced.py SKIPPED [100%]

=================================== FAILURES ===================================
_____________________ test_error_during_generate_with_lock _____________________

self = ModelOutputThunk()

    async def astream(self) -> str:
        """Returns the ModelOutputThunk's partial value including the next chunk(s). Can be used for both async streaming and async non-streaming.
    
        Returns the value of the ModelOutputThunk if streaming is done.
    
        **Note**: Be careful with calling this function. Only call it from one location at a time. This means you shouldn't pass a ModelOutputThunk to
        multiple coroutines/tasks and call astream from those coroutines/tasks simultaneously. We have considered solutions to this but are waiting until
        we see this error happen in a real use case.
    
        Raises:
            Exception: Propagates any errors from the underlying inference engine api request.
            RuntimeError: If called when the ModelOutputThunk's generate function is not async compatible.
        """
        if self._computed:
            assert self.value is not None  # If computed, the value cannot be None.
            return self.value
    
        if not self._generate_type == GenerateType.ASYNC:
            raise RuntimeError(
                f"Cannot use `ModelOutputThunk.astream()` when the generate function is using `{self._generate_type.name}`"
            )
        # Beginning value
        beginning_length = (
            0 if self._underlying_value is None else len(str(self._underlying_value))
        )  # type: ignore
    
        exception_to_raise = None
        try:
            # Type of the chunk depends on the backend.
            chunks: list[Any | None] = []
            while True:
                try:
                    item = self._async_queue.get_nowait()
                    chunks.append(item)
                except asyncio.QueueEmpty:
                    # We've exhausted the current items in the queue.
                    break
    
            # Make sure we always get the minimum chunk size.
            while len(chunks) <= self._chunk_size:
                if len(chunks) > 0:
                    if chunks[-1] is None or isinstance(chunks[-1], Exception):
                        break  # Hit sentinel value or an error.
                    # We could switch to relying on the `done` / `finish_reason` field of chunks,
                    # but that forces us to know about the chunk type here. Prefer sentinel values
                    # for now.
    
                item = await self._async_queue.get()
                chunks.append(item)
    
            # Process the sentinel value if it's there.
            if chunks[-1] is None:
                chunks.pop()  # Remove the sentinel value.
                self._computed = True
    
                # Shouldn't be needed, but cancel the Tasks this ModelOutputThunk relied on.
                if self._generate is not None:
                    self._generate.cancel()
                if self._generate_extra is not None:
                    # Covers an hf edge case. The task is done generating anything useful but isn't `done` yet.
                    await self._generate_extra
                    self._generate_extra.cancel()
    
                # If ModelOutputThunks get too bulky, we can do additional cleanup here
                # and set fields to None.
    
            elif isinstance(chunks[-1], Exception):
                # Mark as computed so post_process runs in finally block
                self._computed = True
                # Store exception to re-raise after cleanup
                exception_to_raise = chunks[-1]
    
            for chunk in chunks:
                assert self._process is not None
>               await self._process(self, chunk)

mellea/core/base.py:331: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <mellea.backends.huggingface.LocalHFBackend object at 0x14ef36ebe840>
mot = ModelOutputThunk(), chunk = Exception('Oops!')
input_ids = tensor([[49152,  2946, 49153, 39558,   390, 17071,  2821,    44, 30468,   225,
            36,    34,    36,    38,   ... 49153,  7656,     0,   203, 49152,   496, 49153,
          7656,     0,   203, 49152, 17594, 49153]], device='cuda:0')

    async def processing(
        self, mot: ModelOutputThunk, chunk: str | GenerateDecoderOnlyOutput, input_ids
    ):
        """Process the returned chunks or the complete response."""
        if mot._underlying_value is None:
            mot._underlying_value = ""
    
        # Because we use the AsyncTextIteratorStreamer, streaming responses are of type str;
        # and already decoded.
        if isinstance(chunk, str):
            mot._underlying_value += chunk
        else:
            # Otherwise, it's a non-streaming request. Decode it here.
            mot._meta["hf_output"] = chunk
            mot._underlying_value += self._tokenizer.decode(
>               chunk.sequences[0, input_ids.shape[1] :], skip_special_tokens=True
                ^^^^^^^^^^^^^^^
            )
E           AttributeError: 'Exception' object has no attribute 'sequences'

mellea/backends/huggingface.py:896: AttributeError

During handling of the above exception, another exception occurred:

backend = <mellea.backends.huggingface.LocalHFBackend object at 0x14f20d968c50>

    @pytest.mark.qualitative
    async def test_error_during_generate_with_lock(backend) -> None:
        # Create local versions of these objects so that mocking
        # doesn't impact other functions. Don't do this in regular code,
        # the copying is complex.
        b: LocalHFBackend = copy(backend)
        model = copy(b._model)
        b._model = model
        b._model.set_adapter([])
        b._added_adapters = {}
        b._loaded_adapters = {}
        b.add_adapter(
            GraniteCommonAdapter("requirement_check", base_model_name=b.base_model_name)
        )
    
        regular_generate = b._model.generate
    
        def generate_and_raise_exc(*args, **kwargs):
            """Will generate like usual for the intrinsic request. Will fail for the regular generation request."""
            if "max_new_tokens" in kwargs:
                return regular_generate(*args, **kwargs)  # type: ignore
            raise Exception("Oops!")
    
        b._model.generate = Mock(side_effect=generate_and_raise_exc)
        assert not isinstance(backend._model, Mock), (
            "mocking went wrong; backend fixture changed; other tests may fail"
        )
    
        # Set up the inputs.
        ctx = ChatContext().add(Message("user", "hello"))
        act = CBlock("hello")
        req_intrinsic = Intrinsic("requirement_check", {"requirement": "did nothing"})
    
        reg_mot, _ = await b.generate_from_context(act, ctx)
        req_mot, _ = await b.generate_from_context(req_intrinsic, ctx)
    
        with pytest.raises(Exception, match="Oops!"):
>           await reg_mot.avalue()

test/backends/test_huggingface.py:531: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
mellea/core/base.py:251: in avalue
    await self.astream()
mellea/core/base.py:338: in astream
    await self._post_process(self)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <mellea.backends.huggingface.LocalHFBackend object at 0x14ef36ebe840>
mot = ModelOutputThunk()
conversation = [{'content': 'hello', 'role': 'user'}, {'content': 'hello', 'role': 'user'}]
_format = None, tool_calls = False, tools = {}, seed = None
input_ids = tensor([[49152,  2946, 49153, 39558,   390, 17071,  2821,    44, 30468,   225,
            36,    34,    36,    38,   ... 49153,  7656,     0,   203, 49152,   496, 49153,
          7656,     0,   203, 49152, 17594, 49153]], device='cuda:0')

    async def post_processing(
        self,
        mot: ModelOutputThunk,
        conversation: list[dict],
        _format: type[BaseModelSubclass] | None,
        tool_calls: bool,
        tools: dict[str, AbstractMelleaTool],
        seed,
        input_ids,
    ):
        """Called when generation is done."""
        if mot._meta.get("hf_output", None) is None:
            if mot._generate_extra is not None:
                full_output = await mot._generate_extra
                assert isinstance(full_output, GenerateDecoderOnlyOutput)
                mot._meta["hf_output"] = full_output
    
        # The ModelOutputThunk must be computed by this point.
        assert mot.value is not None
    
        # Add an entry to the cache for ALora reuse.
        if self._use_caches and mot._meta.get("hf_output", None) is not None:
>           output_complete = mot._meta["hf_output"].sequences[0]
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E           AttributeError: 'Exception' object has no attribute 'sequences'

mellea/backends/huggingface.py:921: AttributeError

During handling of the above exception, another exception occurred:

backend = <mellea.backends.huggingface.LocalHFBackend object at 0x14f20d968c50>

    @pytest.mark.qualitative
    async def test_error_during_generate_with_lock(backend) -> None:
        # Create local versions of these objects so that mocking
        # doesn't impact other functions. Don't do this in regular code,
        # the copying is complex.
        b: LocalHFBackend = copy(backend)
        model = copy(b._model)
        b._model = model
        b._model.set_adapter([])
        b._added_adapters = {}
        b._loaded_adapters = {}
        b.add_adapter(
            GraniteCommonAdapter("requirement_check", base_model_name=b.base_model_name)
        )
    
        regular_generate = b._model.generate
    
        def generate_and_raise_exc(*args, **kwargs):
            """Will generate like usual for the intrinsic request. Will fail for the regular generation request."""
            if "max_new_tokens" in kwargs:
                return regular_generate(*args, **kwargs)  # type: ignore
            raise Exception("Oops!")
    
        b._model.generate = Mock(side_effect=generate_and_raise_exc)
        assert not isinstance(backend._model, Mock), (
            "mocking went wrong; backend fixture changed; other tests may fail"
        )
    
        # Set up the inputs.
        ctx = ChatContext().add(Message("user", "hello"))
        act = CBlock("hello")
        req_intrinsic = Intrinsic("requirement_check", {"requirement": "did nothing"})
    
        reg_mot, _ = await b.generate_from_context(act, ctx)
        req_mot, _ = await b.generate_from_context(req_intrinsic, ctx)
    
>       with pytest.raises(Exception, match="Oops!"):
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E       AssertionError: Regex pattern did not match.
E         Expected regex: 'Oops!'
E         Actual message: "'Exception' object has no attribute 'sequences'"

test/backends/test_huggingface.py:530: AssertionError
----------------------------- Captured stderr call -----------------------------

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=============================== warnings summary ===============================
docs/examples/conftest.py:191
  /proj/dmfexp/eiger/users/jonesn/mellea-c/docs/examples/conftest.py:191: PytestRemovedIn9Warning: The (path: py.path.local) argument is deprecated, please use (collection_path: pathlib.Path)
  see https://docs.pytest.org/en/latest/deprecations.html#py-path-local-arguments-for-hooks-replaced-with-pathlib-path
    def pytest_ignore_collect(collection_path, path, config):

docs/examples/conftest.py:225
  /proj/dmfexp/eiger/users/jonesn/mellea-c/docs/examples/conftest.py:225: PytestRemovedIn9Warning: The (path: py.path.local) argument is deprecated, please use (module_path: pathlib.Path)
  see https://docs.pytest.org/en/latest/deprecations.html#py-path-local-arguments-for-hooks-replaced-with-pathlib-path
    def pytest_pycollect_makemodule(module_path, path, parent):

test/backends/test_huggingface.py::test_constraint_lora_with_requirement
  <frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute

test/backends/test_huggingface.py::test_constraint_lora_with_requirement
  <frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute

test/backends/test_huggingface.py::test_generate_with_lock
test/stdlib/components/intrinsic/test_rag.py::test_query_rewrite
test/stdlib/components/intrinsic/test_rag.py::test_citations
test/stdlib/components/intrinsic/test_rag.py::test_context_relevance
test/stdlib/components/intrinsic/test_rag.py::test_hallucination_detection
test/stdlib/components/intrinsic/test_rag.py::test_answer_relevance
test/stdlib/components/intrinsic/test_rag.py::test_query_clarification_positive
  /u/jonesn/.conda/envs/mellea/lib/python3.12/site-packages/peft/tuners/tuners_utils.py:285: UserWarning: Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=============================== Skipped Examples ===============================
Examples with the following names were skipped because they cannot be easily run in the pytest framework; please run them manually:
mcp_example.py
mellea_pdf.py
simple_rag_with_filter.py
__init__.py
client.py
python_decompose_result.py
pii_serve.py
m_decomp_result.py
=========================== short test summary info ============================
FAILED test/backends/test_huggingface.py::test_error_during_generate_with_lock
= 1 failed, 35 passed, 3 skipped, 346 deselected, 1 xfailed, 11 warnings in 246.67s (0:04:06) =

------------------------------------------------------------
Sender: LSF System <lsfadmin@p4-r02-n1>
Subject: Job 453153: <___huggingface_tests__marker_> in cluster <BLUEVELA_LSF> Exited

Job <___huggingface_tests__marker_> was submitted from host <login3> by user <jonesn> in cluster <BLUEVELA_LSF> at Tue Feb 10 12:28:25 2026
Job was executed on host(s) <p4-r02-n1>, in queue <normal>, as user <jonesn> in cluster <BLUEVELA_LSF> at Tue Feb 10 12:28:26 2026
</u/jonesn> was used as the home directory.
</proj/dmfexp/eiger/users/jonesn/mellea-c> was used as the working directory.
Started at Tue Feb 10 12:28:26 2026
Terminated at Tue Feb 10 12:32:41 2026
Results reported at Tue Feb 10 12:32:41 2026

Your job looked like:

------------------------------------------------------------
# LSBATCH: User input
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True && source /opt/share/miniforge/etc/profile.d/conda.sh && conda activate mellea && pytest -m huggingface --no-cov -v
------------------------------------------------------------

Exited with exit code 1.

Resource usage summary:

    CPU time :                                   2474.00 sec.
    Max Memory :                                 31923 MB
    Average Memory :                             10266.48 MB
    Total Requested Memory :                     -
    Delta Memory :                               -
    Max Swap :                                   -
    Max Processes :                              4
    Max Threads :                                752
    Run time :                                   262 sec.
    Turnaround time :                            256 sec.

The output (if any) is above this job summary.



PS:

Read file </proj/dmfexp/eiger/users/jonesn/mellea-c/logs/___huggingface_tests__marker__453153.stderr> for stderr output of this job.



=== STDERR ===
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute

[planetf1]

The CI error actually exhibits the same error handling problem I raised in 432, albeit the root cause here being a model not found -- fixed.

[planetf1]

CI & cuda runs are clean

[jakelorocco]

lgtm; agree that the test issue is unrelated to this work