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llama : add PLaMo model (ggml-org#3557)
* add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <[email protected]>
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‎README.md‎

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@@ -102,6 +102,7 @@ as the main playground for developing new features for the [ggml](https://github
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- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
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- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
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- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
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- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
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**Multimodal models:**
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‎convert-hf-to-gguf.py‎

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@@ -184,6 +184,8 @@ def from_model_architecture(model_architecture):
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return MixtralModel
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if model_architecture == "PhiForCausalLM":
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return Phi2Model
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if model_architecture == "PlamoForCausalLM":
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return PlamoModel
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return Model
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def _is_model_safetensors(self) -> bool:
@@ -225,6 +227,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH:
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return gguf.MODEL_ARCH.LLAMA
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if arch == "PhiForCausalLM":
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return gguf.MODEL_ARCH.PHI2
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if arch == "PlamoForCausalLM":
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return gguf.MODEL_ARCH.PLAMO
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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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@@ -1002,11 +1006,91 @@ def set_gguf_parameters(self):
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self.gguf_writer.add_add_bos_token(False)
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class PlamoModel(Model):
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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def set_gguf_parameters(self):
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hparams = self.hparams
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block_count = hparams["num_hidden_layers"]
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self.gguf_writer.add_name("PLaMo")
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self.gguf_writer.add_context_length(4096) # not in config.json
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self.gguf_writer.add_embedding_length(hparams["hidden_size"])
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self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_head_count(hparams["num_attention_heads"])
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self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
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self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
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def shuffle_attn_q_weight(self, data_torch):
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assert data_torch.size() == (5120, 5120)
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data_torch = data_torch.reshape(8, 5, 128, 5120)
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data_torch = torch.permute(data_torch, (1, 0, 2, 3))
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data_torch = torch.reshape(data_torch, (5120, 5120))
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return data_torch
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def shuffle_attn_output_weight(self, data_torch):
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assert data_torch.size() == (5120, 5120)
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data_torch = data_torch.reshape(5120, 8, 5, 128)
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data_torch = torch.permute(data_torch, (0, 2, 1, 3))
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data_torch = torch.reshape(data_torch, (5120, 5120))
1038+
return data_torch
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def write_tensors(self):
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block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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for name, data_torch in self.get_tensors():
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if "self_attn.rotary_emb.inv_freq" in name:
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continue
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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# shuffle for broadcasting of gqa in ggml_mul_mat
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if new_name.endswith("attn_q.weight"):
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data_torch = self.shuffle_attn_q_weight(data_torch)
1057+
elif new_name.endswith("attn_output.weight"):
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data_torch = self.shuffle_attn_output_weight(data_torch)
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
1063+
if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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data = data_torch.squeeze().numpy()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
1073+
data = data.astype(np.float32)
1074+
1075+
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
1076+
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
1077+
data = data.astype(np.float32)
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1079+
# if f16 desired, convert any float32 2-dim weight tensors to float16
1080+
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
1081+
data = data.astype(np.float16)
1082+
1083+
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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1085+
self.gguf_writer.add_tensor(new_name, data)
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###### CONVERSION LOGIC ######
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def parse_args() -> argparse.Namespace:
1009-
parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
1092+
parser = argparse.ArgumentParser(
1093+
description="Convert a huggingface model to a GGML compatible file")
10101094
parser.add_argument(
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"--vocab-only", action="store_true",
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help="extract only the vocab",

‎gguf-py/gguf/constants.py‎

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@@ -96,6 +96,7 @@ class MODEL_ARCH(IntEnum):
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STABLELM = auto()
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QWEN = auto()
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PHI2 = auto()
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PLAMO = auto()
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class MODEL_TENSOR(IntEnum):
@@ -142,6 +143,7 @@ class MODEL_TENSOR(IntEnum):
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PLAMO: "plamo",
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}
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147149
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -349,6 +351,21 @@ class MODEL_TENSOR(IntEnum):
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.PLAMO: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.GPT2: [
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# TODO
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],

‎gguf-py/gguf/tensor_mapping.py‎

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@@ -79,6 +79,7 @@ class TensorNameMap:
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"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
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"model.layers.{bid}.ln1", # yi
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"transformer.h.{bid}.ln", # phi2
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"model.layers.layers.{bid}.norm", # plamo
8283
),
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8485
# Attention norm 2
@@ -99,26 +100,29 @@ class TensorNameMap:
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100101
# Attention query
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MODEL_TENSOR.ATTN_Q: (
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"model.layers.{bid}.self_attn.q_proj", # llama-hf
103-
"layers.{bid}.attention.wq", # llama-pth
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"encoder.layer.{bid}.attention.self.query", # bert
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"transformer.h.{bid}.attn.q_proj", # gpt-j
103+
"model.layers.{bid}.self_attn.q_proj", # llama-hf
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"layers.{bid}.attention.wq", # llama-pth
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"encoder.layer.{bid}.attention.self.query", # bert
106+
"transformer.h.{bid}.attn.q_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.q_proj", # plamo
106108
),
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108110
# Attention key
109111
MODEL_TENSOR.ATTN_K: (
110-
"model.layers.{bid}.self_attn.k_proj", # llama-hf
111-
"layers.{bid}.attention.wk", # llama-pth
112-
"encoder.layer.{bid}.attention.self.key", # bert
113-
"transformer.h.{bid}.attn.k_proj", # gpt-j
112+
"model.layers.{bid}.self_attn.k_proj", # llama-hf
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"layers.{bid}.attention.wk", # llama-pth
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"encoder.layer.{bid}.attention.self.key", # bert
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"transformer.h.{bid}.attn.k_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.k_proj", # plamo
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),
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116119
# Attention value
117120
MODEL_TENSOR.ATTN_V: (
118-
"model.layers.{bid}.self_attn.v_proj", # llama-hf
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"layers.{bid}.attention.wv", # llama-pth
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"encoder.layer.{bid}.attention.self.value", # bert
121-
"transformer.h.{bid}.attn.v_proj", # gpt-j
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"model.layers.{bid}.self_attn.v_proj", # llama-hf
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"layers.{bid}.attention.wv", # llama-pth
123+
"encoder.layer.{bid}.attention.self.value", # bert
124+
"transformer.h.{bid}.attn.v_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.v_proj", # plamo
122126
),
123127

124128
# Attention output
@@ -134,12 +138,14 @@ class TensorNameMap:
134138
"transformer.h.{bid}.attn.out_proj", # gpt-j
135139
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
136140
"transformer.h.{bid}.mixer.out_proj", # phi2
141+
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
137142
),
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139144
# Rotary embeddings
140145
MODEL_TENSOR.ATTN_ROT_EMBD: (
141-
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
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"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
146+
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
147+
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
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"model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
143149
),
144150

145151
# Feed-forward norm
@@ -174,6 +180,7 @@ class TensorNameMap:
174180
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
175181
"transformer.h.{bid}.mlp.w1", # qwen
176182
"transformer.h.{bid}.mlp.fc1", # phi2
183+
"model.layers.layers.{bid}.mlp.up_proj", # plamo
177184
),
178185

179186
MODEL_TENSOR.FFN_UP_EXP: (
@@ -186,6 +193,7 @@ class TensorNameMap:
186193
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
187194
"layers.{bid}.feed_forward.w1", # llama-pth
188195
"transformer.h.{bid}.mlp.w2", # qwen
196+
"model.layers.layers.{bid}.mlp.gate_proj", # plamo
189197
),
190198

191199
MODEL_TENSOR.FFN_GATE_EXP: (
@@ -206,6 +214,7 @@ class TensorNameMap:
206214
"transformer.h.{bid}.mlp.fc_out", # gpt-j
207215
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
208216
"transformer.h.{bid}.mlp.fc2", # phi2
217+
"model.layers.layers.{bid}.mlp.down_proj", # plamo
209218
),
210219

211220
MODEL_TENSOR.FFN_DOWN_EXP: (

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