Upload 14 files
Browse files- added_tokens.json +41 -0
- config.json +37 -0
- configuration_pruned_phi.py +84 -0
- generation_config.json +7 -0
- merges.txt +0 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +460 -0
- modeling_pruned_phi.py +28 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer_config.json +328 -0
- vocab.json +0 -0
added_tokens.json
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"\t\t": 50294,
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"\t\t\t": 50293,
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"\t\t\t\t": 50292,
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"\t\t\t\t\t": 50291,
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"\t\t\t\t\t\t": 50290,
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"\t\t\t\t\t\t\t": 50289,
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"\t\t\t\t\t\t\t\t": 50288,
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"\t\t\t\t\t\t\t\t\t": 50287,
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" ": 50257,
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"<|eot_id|>": 50295
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}
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config.json
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{
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"_name_or_path": "/data2/caoty/convert/phi2_0.45_0-31_gsm8k_second",
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"architectures": [
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"PhiPrunedForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_pruned_phi.PhiPrunedConfig",
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"AutoModel": "modeling_pruned_phi.PhiPrunedForCausalLM",
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"AutoModelForCausalLM": "modeling_pruned_phi.PhiPrunedForCausalLM"
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},
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"begin_pruned_layer": 0,
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
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"end_pruned_layer": 32,
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"eos_token_id": 50256,
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"hidden_act": "gelu_new",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "phi",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"partial_rotary_factor": 0.4,
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"qk_layernorm": false,
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"resid_pdrop": 0.1,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.45.2",
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"use_cache": true,
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"vocab_size": 51200
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}
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configuration_pruned_phi.py
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from transformers import PretrainedConfig
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class PhiPrunedConfig(PretrainedConfig):
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model_type = "phi"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=51200,
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hidden_size=2048,
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intermediate_size=8192,
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num_hidden_layers=24,
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="gelu_new",
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max_position_embeddings=2048,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.5,
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qk_layernorm=False,
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bos_token_id=1,
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eos_token_id=2,
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begin_pruned_layer=None,
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end_pruned_layer=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.qk_layernorm = qk_layernorm
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self._rope_scaling_validation()
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self.begin_pruned_layer = begin_pruned_layer
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self.end_pruned_layer = end_pruned_layer
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_validation(self):
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50256,
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"do_sample": true,
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"eos_token_id": 50256,
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"transformers_version": "4.45.2"
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}
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merges.txt
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The diff for this file is too large to render.
See raw diff
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9818d66ab771784d637ebf355b3f9fb863a74799e87750a6ac8e35d6b757443d
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size 1979866336
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model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8975c6e1c84fdd8af23e5c83f305d6441731e21589607a61b2c9db979b54e2e8
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+
size 1953739648
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model-00003-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fb41f56b2e111165c243088b0b494f4c4dd145ca7ff95be9cff94d510d84764d
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| 3 |
+
size 367151112
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model.safetensors.index.json
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|
| 416 |
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|
| 417 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 438 |
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|
| 439 |
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|
| 440 |
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|
| 441 |
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|
| 442 |
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|
| 443 |
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|
| 444 |
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|
| 445 |
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|
| 446 |
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|
| 447 |
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|
| 448 |
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|
| 449 |
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|
| 450 |
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|
| 451 |
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|
| 452 |
+
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|
| 453 |
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|
| 454 |
+
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|
| 455 |
+
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|
| 456 |
+
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|
| 457 |
+
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|
| 458 |
+
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|
| 459 |
+
}
|
| 460 |
+
}
|
modeling_pruned_phi.py
ADDED
|
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|
| 1 |
+
|
| 2 |
+
import torch
|
| 3 |
+
from transformers import PhiForCausalLM
|
| 4 |
+
from .configuration_pruned_phi import PhiPrunedConfig
|
| 5 |
+
import torch.nn as nn
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
class PhiPrunedForCausalLM(PhiForCausalLM):
|
| 9 |
+
config_class = PhiPrunedConfig
|
| 10 |
+
|
| 11 |
+
def __init__(self, config: PhiPrunedConfig):
|
| 12 |
+
super().__init__(config)
|
| 13 |
+
for i in range(32):
|
| 14 |
+
self.model.layers[i].self_attn.dense = nn.Linear(640, 2560, bias=True)
|
| 15 |
+
self.model.layers[i].self_attn.hidden_size = 640
|
| 16 |
+
self.model.layers[i].self_attn.q_proj = nn.Linear(2560, 640, bias=True)
|
| 17 |
+
self.model.layers[i].self_attn.k_proj = nn.Linear(2560, 640, bias=True)
|
| 18 |
+
self.model.layers[i].self_attn.v_proj = nn.Linear(2560, 640, bias=True)
|
| 19 |
+
|
| 20 |
+
self.model.layers[i].mlp.fc1 = nn.Linear(2560, 10240, bias=True)
|
| 21 |
+
self.model.layers[i].mlp.fc2 = nn.Linear(10240, 2560, bias=True)
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
for layer in self.model.layers:
|
| 26 |
+
layer.self_attn.num_heads = layer.self_attn.q_proj.weight.data.shape[0] // layer.self_attn.head_dim
|
| 27 |
+
layer.self_attn.num_key_value_heads = layer.self_attn.k_proj.weight.data.shape[
|
| 28 |
+
0] // layer.self_attn.head_dim
|
special_tokens_map.json
ADDED
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@@ -0,0 +1,24 @@
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|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|endoftext|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|endoftext|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": "<|endoftext|>",
|
| 17 |
+
"unk_token": {
|
| 18 |
+
"content": "<|endoftext|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
}
|
| 24 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,328 @@
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| 1 |
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| 2 |
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| 32 |
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+
"content": "\t\t\t\t",
|
| 294 |
+
"lstrip": false,
|
| 295 |
+
"normalized": true,
|
| 296 |
+
"rstrip": false,
|
| 297 |
+
"single_word": false,
|
| 298 |
+
"special": false
|
| 299 |
+
},
|
| 300 |
+
"50293": {
|
| 301 |
+
"content": "\t\t\t",
|
| 302 |
+
"lstrip": false,
|
| 303 |
+
"normalized": true,
|
| 304 |
+
"rstrip": false,
|
| 305 |
+
"single_word": false,
|
| 306 |
+
"special": false
|
| 307 |
+
},
|
| 308 |
+
"50294": {
|
| 309 |
+
"content": "\t\t",
|
| 310 |
+
"lstrip": false,
|
| 311 |
+
"normalized": true,
|
| 312 |
+
"rstrip": false,
|
| 313 |
+
"single_word": false,
|
| 314 |
+
"special": false
|
| 315 |
+
}
|
| 316 |
+
},
|
| 317 |
+
"bos_token": "<|endoftext|>",
|
| 318 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message + '\n' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '\nAssistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + '\n' }}{% endif %}{% endfor %}",
|
| 319 |
+
"clean_up_tokenization_spaces": true,
|
| 320 |
+
"eos_token": "<|endoftext|>",
|
| 321 |
+
"model_max_length": 2048,
|
| 322 |
+
"pad_token": "<|endoftext|>",
|
| 323 |
+
"padding_side": "left",
|
| 324 |
+
"return_token_type_ids": false,
|
| 325 |
+
"split_special_tokens": false,
|
| 326 |
+
"tokenizer_class": "CodeGenTokenizer",
|
| 327 |
+
"unk_token": "<|endoftext|>"
|
| 328 |
+
}
|
vocab.json
ADDED
|
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|