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cc7200bf12
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
from __future__ import annotations
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from typing import Iterable, TYPE_CHECKING
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf, logger
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@ModelBase.register("CohereForCausalLM")
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class CommandR2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.COMMAND_R
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# max_position_embeddings = 8192 in config.json but model was actually
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# trained on 128k context length
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# aya-23 models don't have model_max_length specified
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self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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@ModelBase.register("Cohere2ForCausalLM")
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class Cohere2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.COHERE2
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
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self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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rotary_pct = self.hparams["rotary_pct"]
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hidden_size = self.hparams["hidden_size"]
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num_attention_heads = self.hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# Cohere2 runtime in llama.cpp expects no bias tensors;
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# the actual weight only contains 0-value tensors as bias, we can skip them
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if name.endswith(".bias"):
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if torch.any(data_torch != 0):
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raise ValueError(f"Bias tensor {name!r} is not zero.")
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logger.debug(f"Skipping bias tensor {name!r} for Cohere2 conversion.")
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return
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yield from super().modify_tensors(data_torch, name, bid)
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