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@ -326,7 +326,7 @@ class Model:
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if isinstance(self.size, dict):
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if hasattr(self.size, "label") and self.size['label']:
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name.append(f"{self.size['label']}")
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elif isinstance(self.size, str) and self.size != "full":
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elif isinstance(self.size, str) and self.size not in ["full","extended"]:
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name.append(self.size)
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if self.experts > 1:
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@ -392,6 +392,8 @@ class Model:
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if self.size == "double":
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return 24
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if self.size == "extended":
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return 16
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return 12
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@property
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@ -231,6 +231,49 @@ def load_engines(training=True, **model_kwargs):
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del state['resps_emb.embeddings.8.weight']
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"""
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"""
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if True:
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remapped_dict = {}
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remapped_indices = [
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(0, 1),
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(1, 2),
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(2, 3),
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(3, 5),
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(4, 6),
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(5, 7),
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(6, 9),
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(7, 10),
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(8, 11),
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(9, 13),
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(10, 14),
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(11, 15),
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]
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for src, dst in remapped_indices:
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remapped_dict[f"model.layers.{dst}.input_layernorm.weight"] = state[f"model.layers.{src}.input_layernorm.weight"]
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remapped_dict[f"model.layers.{dst}.self_attn.k_proj.weight"] = state[f"model.layers.{src}.self_attn.k_proj.weight"]
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remapped_dict[f"model.layers.{dst}.self_attn.q_proj.weight"] = state[f"model.layers.{src}.self_attn.q_proj.weight"]
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remapped_dict[f"model.layers.{dst}.self_attn.v_proj.weight"] = state[f"model.layers.{src}.self_attn.v_proj.weight"]
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remapped_dict[f"model.layers.{dst}.self_attn.o_proj.weight"] = state[f"model.layers.{src}.self_attn.o_proj.weight"]
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remapped_dict[f"model.layers.{dst}.post_attention_layernorm.weight"] = state[f"model.layers.{src}.post_attention_layernorm.weight"]
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remapped_dict[f"model.layers.{dst}.mlp.down_proj.weight"] = state[f"model.layers.{src}.mlp.down_proj.weight"]
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remapped_dict[f"model.layers.{dst}.mlp.gate_proj.weight"] = state[f"model.layers.{src}.mlp.gate_proj.weight"]
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remapped_dict[f"model.layers.{dst}.mlp.up_proj.weight"] = state[f"model.layers.{src}.mlp.up_proj.weight"]
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del state[f"model.layers.{src}.input_layernorm.weight"]
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del state[f"model.layers.{src}.self_attn.k_proj.weight"]
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del state[f"model.layers.{src}.self_attn.q_proj.weight"]
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del state[f"model.layers.{src}.self_attn.v_proj.weight"]
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del state[f"model.layers.{src}.self_attn.o_proj.weight"]
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del state[f"model.layers.{src}.post_attention_layernorm.weight"]
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del state[f"model.layers.{src}.mlp.down_proj.weight"]
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del state[f"model.layers.{src}.mlp.gate_proj.weight"]
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del state[f"model.layers.{src}.mlp.up_proj.weight"]
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for k, v in remapped_dict.items():
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state[k] = v
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"""
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model.load_state_dict(state, strict=cfg.trainer.strict_loading)
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# load lora weights if exists
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