model_arch = gguf.MODEL_ARCH.GPTNEOX
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
-
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(
int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
self.gguf_writer.add_embedding_length(n_embed)
self.gguf_writer.add_feed_forward_length(4 * n_embed)
- self.gguf_writer.add_block_count(self.hparams["n_layer"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_unk_token_id(0)
def set_gguf_parameters(self):
- block_count = self.hparams["n_layers"]
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
model_arch = gguf.MODEL_ARCH.FALCON
def set_gguf_parameters(self):
- block_count = self.hparams.get("num_hidden_layers")
- if block_count is None:
- block_count = self.hparams["n_layer"] # old name
-
n_head = self.hparams.get("num_attention_heads")
if n_head is None:
n_head = self.hparams["n_head"] # old name
self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head_kv)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
model_arch = gguf.MODEL_ARCH.STARCODER
def set_gguf_parameters(self):
- block_count = self.hparams["n_layer"]
-
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(1)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
multiple_of = 256
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
- block_count = self.hparams["n_layer"]
-
# refact uses Alibi. So this is from config.json which might be used by training.
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(ff_dim)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(1)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
def set_gguf_parameters(self):
ffn_config = self.hparams["ffn_config"]
attn_config = self.hparams["attn_config"]
- self.gguf_writer.add_block_count(self.hparams["n_layers"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
def set_gguf_parameters(self):
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
- self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
model_arch = gguf.MODEL_ARCH.GPT2
def set_gguf_parameters(self):
- self.gguf_writer.add_block_count(self.hparams["n_layer"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["n_ctx"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
model_arch = gguf.MODEL_ARCH.PHI2
def set_gguf_parameters(self):
- block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
-
rot_pct = self.find_hparam(["partial_rotary_factor"])
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(4 * n_embd)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
- block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
-
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"]))
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head_kv)
self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_context_length(4096) # not in config.json
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
# Which layers are Mamba layers
num_attention_heads = []
if mamba_enabled:
- for i in range(block_count):
- if block_count <= (mamba_step // 2):
+ for i in range(self.block_count):
+ if self.block_count <= (mamba_step // 2):
# use attention in last layer
- is_mamba = (i != block_count - 1)
+ is_mamba = (i != self.block_count - 1)
else:
is_mamba = (i % mamba_step) != (mamba_step // 2)
if is_mamba:
self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))
self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))
self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000))
model_arch = gguf.MODEL_ARCH.CODESHELL
def set_gguf_parameters(self):
- block_count = self.hparams["n_layer"]
-
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
def set_gguf_parameters(self):
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
- self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
def set_gguf_parameters(self):
hparams = self.hparams
- block_count = hparams["num_hidden_layers"]
# some default values are not specified in the hparams
self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
self._set_vocab_rwkv_world()
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
head_size = self.hparams["head_size"]
hidden_size = self.hparams["hidden_size"]
layer_norm_eps = self.hparams["layer_norm_epsilon"]
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers)
self.gguf_writer.add_wkv_head_size(head_size)
self._set_vocab_gpt2()
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
num_attention_heads = self.hparams["num_attention_heads"]
num_key_value_heads = self.hparams["num_key_value_heads"]
hidden_size = self.hparams["hidden_size"]
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_wkv_head_size(head_size)
self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
return max(1, round(hidden_size ** exponent * multiplier / 32)) * 32
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
try:
head_size = self.hparams["head_size"]
layer_norm_eps = self.hparams["layer_norm_epsilon"]
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
self.gguf_writer.add_wkv_head_size(head_size)
self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
self._set_vocab_gpt2()
def set_gguf_parameters(self):
- block_count = self.hparams["num_hidden_layers"]
hidden_size = self.hparams["hidden_size"]
head_size = self.hparams["head_size"]
rms_norm_eps = self.hparams["rms_norm_eps"]
# RWKV isn't context limited
self.gguf_writer.add_context_length(1048576)
self.gguf_writer.add_embedding_length(hidden_size)
- self.gguf_writer.add_block_count(block_count)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_wkv_head_size(head_size)
self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
self.gguf_writer.add_context_length(n_ctx)
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
- self.gguf_writer.add_block_count(self.hparams["num_layers"])
+ self.gguf_writer.add_block_count(self.block_count)
if (dec_n_layer := self.hparams.get("num_decoder_layers")) is not None:
self.gguf_writer.add_decoder_block_count(dec_n_layer)
self.gguf_writer.add_head_count(self.hparams["num_heads"])
self.gguf_writer.add_context_length(n_ctx)
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
- self.gguf_writer.add_block_count(self.hparams["num_layers"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(self.hparams["num_heads"])
self.gguf_writer.add_key_length(self.hparams["d_kv"])
self.gguf_writer.add_value_length(self.hparams["d_kv"])
self._set_vocab_gpt2()
def set_gguf_parameters(self):
- self.gguf_writer.add_block_count(self.hparams["n_layer"])
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
self.gguf_writer.add_embedding_length(n_embed)
self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
- self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
num_kv_heads = hparams.get("num_key_value_heads", num_heads)
layer_norm_eps = hparams["layer_norm_epsilon"]
intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim
- num_layers = hparams["num_layers"]
# ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0
# attention_dropout_rate = hparams["attention_dropout"]
# ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0
self.gguf_writer.add_context_length(max_position_embeddings)
self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps)
self.gguf_writer.add_feed_forward_length(intermediate_size)
- self.gguf_writer.add_block_count(num_layers)
+ self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_file_type(self.ftype)
if (rope_theta := self.hparams.get("rope_theta")) is not None: