vbatts/llama.cpp
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fix: lint

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younesbelkada 2024-08-18 14:20:37 +00:00
parent f7d2e9105f
commit 9e22bb7ed6
1 changed files with 3 additions and 3 deletions
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6
convert_hf_to_gguf.py
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@ -2731,7 +2731,7 @@ class MambaModel(Model):
else: else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present # Use the GPT-NeoX tokenizer when no tokenizer files are present
self._set_vocab_builtin("gpt-neox", vocab_size) self._set_vocab_builtin("gpt-neox", vocab_size)
def set_gguf_parameters(self): def set_gguf_parameters(self):
d_model = self.find_hparam(["hidden_size", "d_model"]) d_model = self.find_hparam(["hidden_size", "d_model"])
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
@ -2741,7 +2741,7 @@ class MambaModel(Model):
# ref: https://stackoverflow.com/a/17511341/22827863 # ref: https://stackoverflow.com/a/17511341/22827863
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
use_dt_b_c_norm = False use_dt_b_c_norm = False
# For falconmamba we do apply RMS norm on B / DT and C layers # For falconmamba we do apply RMS norm on B / DT and C layers
if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",): if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",):
@ -3858,7 +3858,7 @@ class ExaoneModel(Model):
self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32)) self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
super().prepare_tensors() super().prepare_tensors()
###### CONVERSION LOGIC ###### ###### CONVERSION LOGIC ######