add 4 medium models

brainscore_language/models/falcon7b/__init__.py

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+from brainscore_language import model_registry
+from brainscore_language import ArtificialSubject
+from brainscore_language.model_helpers.huggingface import HuggingfaceSubject
+
+# Falcon-7B: 32 transformer layers, hidden size 4544.
+# Layer 31 (last) chosen as default mapping pending benchmark-driven selection.
+model_registry['falcon-7b'] = lambda: HuggingfaceSubject(
+    model_id='tiiuae/falcon-7b',
+    region_layer_mapping={
+        ArtificialSubject.RecordingTarget.language_system: 'transformer.h.31'
+    },
+)

brainscore_language/models/falcon7b/metadata.yml

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+models:
+  falcon7b:
+    architecture: DCNN
+    model_family: falcon7b
+    total_parameter_count: 1234567
+    trainable_parameter_count: 1234567
+    total_layers: 55
+    trainable_layers: 40
+    model_size_mb: 1202
+    training_dataset: null
+    task_specialization: null
+    brainscore_link: https://github.com/brain-score/language/tree/master/brainscore_language/models/falcon7b
+    huggingface_link: null
+    extra_notes: Temporary hardcoded metadata - will be replaced with actual generation
+    runnable: true

brainscore_language/models/falcon7b/test.py

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+import numpy as np
+import pytest
+
+from brainscore_language import load_model
+from brainscore_language.artificial_subject import ArtificialSubject
+
+
+@pytest.mark.memory_intense
+def test_load_model():
+    model = load_model('falcon-7b')
+    assert model is not None
+
+
+@pytest.mark.memory_intense
+def test_neural():
+    model = load_model('falcon-7b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy dog']
+    model.start_neural_recording(
+        recording_target=ArtificialSubject.RecordingTarget.language_system,
+        recording_type=ArtificialSubject.RecordingType.fMRI,
+    )
+    representations = model.digest_text(text)['neural']
+    assert len(representations['presentation']) == 3
+    np.testing.assert_array_equal(representations['stimulus'], text)
+    assert len(representations['neuroid']) == 4544
+
+
+@pytest.mark.memory_intense
+def test_next_word():
+    model = load_model('falcon-7b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy']
+    model.start_behavioral_task(task=ArtificialSubject.Task.next_word)
+    next_words = model.digest_text(text)['behavior']
+    assert len(next_words) == 3
+    for word in next_words.values:
+        assert isinstance(word, str)
+        assert len(word.strip()) > 0

brainscore_language/models/opt6_7b/__init__.py

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+from brainscore_language import model_registry
+from brainscore_language import ArtificialSubject
+from brainscore_language.model_helpers.huggingface import HuggingfaceSubject
+
+# OPT-6.7B: 32 transformer layers, hidden size 4096.
+# Layer 31 (last) chosen as default mapping pending benchmark-driven selection.
+model_registry['opt-6.7b'] = lambda: HuggingfaceSubject(
+    model_id='facebook/opt-6.7b',
+    region_layer_mapping={
+        ArtificialSubject.RecordingTarget.language_system: 'model.decoder.layers.31'
+    },
+)

brainscore_language/models/opt6_7b/metadata.yml

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+models:
+  opt6_7b:
+    architecture: DCNN
+    model_family: opt6_7b
+    total_parameter_count: 1234567
+    trainable_parameter_count: 1234567
+    total_layers: 55
+    trainable_layers: 40
+    model_size_mb: 1202
+    training_dataset: null
+    task_specialization: null
+    brainscore_link: https://github.com/brain-score/language/tree/master/brainscore_language/models/opt6_7b
+    huggingface_link: null
+    extra_notes: Temporary hardcoded metadata - will be replaced with actual generation
+    runnable: true

brainscore_language/models/opt6_7b/test.py

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+import numpy as np
+import pytest
+
+from brainscore_language import load_model
+from brainscore_language.artificial_subject import ArtificialSubject
+
+
+@pytest.mark.memory_intense
+def test_load_model():
+    model = load_model('opt-6.7b')
+    assert model is not None
+
+
+@pytest.mark.memory_intense
+def test_neural():
+    model = load_model('opt-6.7b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy dog']
+    model.start_neural_recording(
+        recording_target=ArtificialSubject.RecordingTarget.language_system,
+        recording_type=ArtificialSubject.RecordingType.fMRI,
+    )
+    representations = model.digest_text(text)['neural']
+    assert len(representations['presentation']) == 3
+    np.testing.assert_array_equal(representations['stimulus'], text)
+    assert len(representations['neuroid']) == 4096
+
+
+@pytest.mark.memory_intense
+def test_next_word():
+    model = load_model('opt-6.7b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy']
+    model.start_behavioral_task(task=ArtificialSubject.Task.next_word)
+    next_words = model.digest_text(text)['behavior']
+    assert len(next_words) == 3
+    for word in next_words.values:
+        assert isinstance(word, str)
+        assert len(word.strip()) > 0

brainscore_language/models/pythia12b/__init__.py

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+from brainscore_language import model_registry
+from brainscore_language import ArtificialSubject
+from brainscore_language.model_helpers.huggingface import HuggingfaceSubject
+
+# Pythia-12B: 36 transformer layers, hidden size 5120.
+# Layer 35 (last) chosen as default mapping pending benchmark-driven selection.
+model_registry['pythia-12b'] = lambda: HuggingfaceSubject(
+    model_id='EleutherAI/pythia-12b',
+    region_layer_mapping={
+        ArtificialSubject.RecordingTarget.language_system: 'gpt_neox.layers.35'
+    },
+)

brainscore_language/models/pythia12b/metadata.yml

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+models:
+  pythia12b:
+    architecture: DCNN
+    model_family: pythia12b
+    total_parameter_count: 1234567
+    trainable_parameter_count: 1234567
+    total_layers: 55
+    trainable_layers: 40
+    model_size_mb: 1202
+    training_dataset: null
+    task_specialization: null
+    brainscore_link: https://github.com/brain-score/language/tree/master/brainscore_language/models/pythia12b
+    huggingface_link: null
+    extra_notes: Temporary hardcoded metadata - will be replaced with actual generation
+    runnable: true

brainscore_language/models/pythia12b/test.py

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+import numpy as np
+import pytest
+
+from brainscore_language import load_model
+from brainscore_language.artificial_subject import ArtificialSubject
+
+
+@pytest.mark.memory_intense
+def test_load_model():
+    model = load_model('pythia-12b')
+    assert model is not None
+
+
+@pytest.mark.memory_intense
+def test_neural():
+    model = load_model('pythia-12b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy dog']
+    model.start_neural_recording(
+        recording_target=ArtificialSubject.RecordingTarget.language_system,
+        recording_type=ArtificialSubject.RecordingType.fMRI,
+    )
+    representations = model.digest_text(text)['neural']
+    assert len(representations['presentation']) == 3
+    np.testing.assert_array_equal(representations['stimulus'], text)
+    assert len(representations['neuroid']) == 5120
+
+
+@pytest.mark.memory_intense
+def test_next_word():
+    model = load_model('pythia-12b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy']
+    model.start_behavioral_task(task=ArtificialSubject.Task.next_word)
+    next_words = model.digest_text(text)['behavior']
+    assert len(next_words) == 3
+    for word in next_words.values:
+        assert isinstance(word, str)
+        assert len(word.strip()) > 0

brainscore_language/models/qwen2_5_3b/__init__.py

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+from brainscore_language import model_registry
+from brainscore_language import ArtificialSubject
+from brainscore_language.model_helpers.huggingface import HuggingfaceSubject
+
+# Qwen2.5-3B: 36 transformer layers, hidden size 2048.
+# Layer 35 (last) chosen as default mapping pending benchmark-driven selection.
+model_registry['qwen2.5-3b'] = lambda: HuggingfaceSubject(
+    model_id='Qwen/Qwen2.5-3B',
+    region_layer_mapping={
+        ArtificialSubject.RecordingTarget.language_system: 'model.layers.35'
+    },
+)

brainscore_language/models/qwen2_5_3b/metadata.yml

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+models:
+  qwen2_5_3b:
+    architecture: DCNN
+    model_family: qwen2_5_3b
+    total_parameter_count: 1234567
+    trainable_parameter_count: 1234567
+    total_layers: 55
+    trainable_layers: 40
+    model_size_mb: 1202
+    training_dataset: null
+    task_specialization: null
+    brainscore_link: https://github.com/brain-score/language/tree/master/brainscore_language/models/qwen2_5_3b
+    huggingface_link: null
+    extra_notes: Temporary hardcoded metadata - will be replaced with actual generation
+    runnable: true

brainscore_language/models/qwen2_5_3b/test.py

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+import numpy as np
+import pytest
+
+from brainscore_language import load_model
+from brainscore_language.artificial_subject import ArtificialSubject
+
+
+@pytest.mark.memory_intense
+def test_load_model():
+    model = load_model('qwen2.5-3b')
+    assert model is not None
+
+
+@pytest.mark.memory_intense
+def test_neural():
+    model = load_model('qwen2.5-3b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy dog']
+    model.start_neural_recording(
+        recording_target=ArtificialSubject.RecordingTarget.language_system,
+        recording_type=ArtificialSubject.RecordingType.fMRI,
+    )
+    representations = model.digest_text(text)['neural']
+    assert len(representations['presentation']) == 3
+    np.testing.assert_array_equal(representations['stimulus'], text)
+    assert len(representations['neuroid']) == 2048
+
+
+@pytest.mark.memory_intense
+def test_next_word():
+    model = load_model('qwen2.5-3b')
+    text = ['the quick brown fox', 'jumps over', 'the lazy']
+    model.start_behavioral_task(task=ArtificialSubject.Task.next_word)
+    next_words = model.digest_text(text)['behavior']
+    assert len(next_words) == 3
+    for word in next_words.values:
+        assert isinstance(word, str)
+        assert len(word.strip()) > 0