Update configuration_steerling.py

configuration_steerling.py

@@ -0,0 +1,102 @@
+from transformers import PretrainedConfig
+
+
+class SteerlingConfig(PretrainedConfig):
+    model_type = "steerling"
+
+    def __init__(
+        self,
+        vocab_size=100281,
+        interpretable=True,
+        n_layers=32,
+        n_head=32,
+        n_embd=4096,
+        n_kv_heads=4,
+        block_size=4096,
+        diff_block_size=64,
+        use_rms_norm=True,
+        norm_eps=1e-05,
+        norm_order="post",
+        use_qk_norm=True,
+        use_rope=True,
+        rope_base=500000.0,
+        rope_full_precision=True,
+        clip_qkv=10.0,
+        mlp_type="swiglu",
+        activation="gelu",
+        mlp_ratio=4,
+        intermediate_size=None,
+        use_bias=False,
+        weight_sharing=True,
+        mask_token_id=100280,
+        endofchunk_token_id=100279,
+        n_concepts=33732,
+        n_unknown_concepts=101196,
+        concept_dim=4096,
+        use_attention_known=False,
+        use_attention_unknown=False,
+        topk_known=16,
+        topk_known_features=32,
+        unknown_topk=128,
+        use_unknown=True,
+        apply_topk_to_unknown=True,
+        topk_on_logits=False,
+        factorize_unknown=True,
+        factorize_rank=256,
+        use_epsilon_correction=True,
+        concept_block_size=4096,
+        pad_multiple=16,
+        store_unknown_weights=False,
+        inject_layer=16,
+        inject_alpha=1.0,
+        **kwargs,
+    ):
+        self.interpretable = interpretable
+        self.n_layers = n_layers
+        self.n_head = n_head
+        self.n_embd = n_embd
+        self.n_kv_heads = n_kv_heads
+        self.block_size = block_size
+        self.diff_block_size = diff_block_size
+        self.use_rms_norm = use_rms_norm
+        self.norm_eps = norm_eps
+        self.norm_order = norm_order
+        self.use_qk_norm = use_qk_norm
+        self.use_rope = use_rope
+        self.rope_base = rope_base
+        self.rope_full_precision = rope_full_precision
+        self.clip_qkv = clip_qkv
+        self.mlp_type = mlp_type
+        self.activation = activation
+        self.mlp_ratio = mlp_ratio
+        self.intermediate_size = intermediate_size
+        self.use_bias = use_bias
+        self.weight_sharing = weight_sharing
+        self.mask_token_id = mask_token_id
+        self.endofchunk_token_id = endofchunk_token_id
+        self.n_concepts = n_concepts
+        self.n_unknown_concepts = n_unknown_concepts
+        self.concept_dim = concept_dim
+        self.use_attention_known = use_attention_known
+        self.use_attention_unknown = use_attention_unknown
+        self.topk_known = topk_known
+        self.topk_known_features = topk_known_features
+        self.unknown_topk = unknown_topk
+        self.use_unknown = use_unknown
+        self.apply_topk_to_unknown = apply_topk_to_unknown
+        self.topk_on_logits = topk_on_logits
+        self.factorize_unknown = factorize_unknown
+        self.factorize_rank = factorize_rank
+        self.use_epsilon_correction = use_epsilon_correction
+        self.concept_block_size = concept_block_size
+        self.pad_multiple = pad_multiple
+        self.store_unknown_weights = store_unknown_weights
+        self.inject_layer = inject_layer
+        self.inject_alpha = inject_alpha
+        super().__init__(
+            vocab_size=vocab_size,
+            pad_token_id=kwargs.pop("pad_token_id", 100277),
+            bos_token_id=kwargs.pop("bos_token_id", 100278),
+            eos_token_id=kwargs.pop("eos_token_id", 100257),
+            **kwargs,
+        )