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convert : fix persimmon conversion to write correct n_rot

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Georgi Gerganov 2024-01-12 13:00:51 +02:00
parent 0cb764e4ab
commit eea19039fc
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2 changed files with 15 additions and 1 deletions
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9
convert-hf-to-gguf.py
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@ -817,10 +817,17 @@ class PersimmonModel(Model):
hidden_size = self.hparams["hidden_size"]
self.gguf_writer.add_name('persimmon-8b-chat')
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hidden_size)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
# NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller
# than the head size?
# ref: https://github.com/ggerganov/llama.cpp/pull/4889
#self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])

7
gguf-py/gguf/tensor_mapping.py
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@ -57,6 +57,7 @@ class TensorNameMap:
"transformer.norm_f", # mpt
"ln_f", # refact bloom qwen gpt2
"language_model.encoder.final_layernorm", # persimmon
"model.final_layernorm", # persimmon
"lm_head.ln", # phi2
),
@ -98,6 +99,7 @@ class TensorNameMap:
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
"model.layers.{bid}.self_attn.query_key_value", # persimmon
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
),
@ -141,6 +143,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
"model.layers.{bid}.self_attn.dense", # persimmon
"h.{bid}.attn.c_proj", # gpt2
"transformer.h.{bid}.mixer.out_proj", # phi2
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
@ -184,6 +187,7 @@ class TensorNameMap:
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
"model.layers.{bid}.mlp.dense_h_to_4h", # persimmon
"transformer.h.{bid}.mlp.w1", # qwen
"h.{bid}.mlp.c_fc", # gpt2
"transformer.h.{bid}.mlp.fc1", # phi2
@ -225,6 +229,7 @@ class TensorNameMap:
"encoder.layer.{bid}.output.dense", # bert
"transformer.h.{bid}.mlp.fc_out", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
"model.layers.{bid}.mlp.dense_4h_to_h", # persimmon
"h.{bid}.mlp.c_proj", # gpt2
"transformer.h.{bid}.mlp.fc2", # phi2
"model.layers.layers.{bid}.mlp.down_proj", # plamo
@ -237,10 +242,12 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
"model.layers.{bid}.self_attn.q_layernorm", # persimmon
),
MODEL_TENSOR.ATTN_K_NORM: (
"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
"model.layers.{bid}.self_attn.k_layernorm", # persimmon
),
MODEL_TENSOR.ROPE_FREQS: (