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Model: yibinlei/effir-mistral-drop-16-attn
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---
library_name: transformers
base_model: mistralai/Mistral-7B-v0.1
tags:
- effir
- retrieval
- mteb
- mistral
- peft
- lora
- custom_code
---
# EffiR Mistral Drop 16 Attn
EffiR Mistral dense retriever with direct layer dropping.
Use `trust_remote_code=True`.
```python
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from huggingface_hub import hf_hub_download
import torch
repo = "yibinlei/effir-mistral-drop-16-attn"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
config = AutoConfig.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo,
config=config,
trust_remote_code=True,
attn_implementation="eager",
torch_dtype=torch.bfloat16,
)
input_emb_path = hf_hub_download(repo, "embedding/input_emb.pth")
model.set_input_embeddings(torch.load(input_emb_path, map_location="cpu"))
model = PeftModel.from_pretrained(model, repo)
model = model.merge_and_unload()
```

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{
"alpha_pattern": {},
"auto_mapping": {
"base_model_class": "MistralForCausalLM",
"parent_library": "transformers_modules.mistral-drop.modeling_dropped_mistral"
},
"base_model_name_or_path": "yibinlei/effir-mistral-drop-16-attn",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 64.0,
"lora_dropout": 0.1,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 32,
"rank_pattern": {},
"revision": null,
"target_modules": [
"o_proj",
"up_proj",
"q_proj",
"gate_proj",
"down_proj",
"k_proj",
"v_proj"
],
"task_type": null,
"use_dora": false,
"use_rslora": false
}

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{
"<instruct>": 32000,
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"<response>": 32002
}

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config.json Normal file
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{
"architectures": [
"MistralForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_dropped_mistral.MistralConfig",
"AutoModelForCausalLM": "modeling_dropped_mistral.MistralForCausalLM"
},
"drop_mlp_list": null,
"drop_attn_list": [
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30
],
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.34.0.dev0",
"use_cache": true,
"vocab_size": 32000
}

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# coding=utf-8
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" transformers==4.38.1"""
""" Mistral model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
"mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
}
class MistralConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MistralModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import MistralModel, MistralConfig
>>> # Initializing a Mistral 7B style configuration
>>> configuration = MistralConfig()
>>> # Initializing a model from the Mistral 7B style configuration
>>> model = MistralModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mistral"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
sliding_window=4096,
attention_dropout=0.0,
drop_mlp_list=None,
drop_attn_list=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
#####################################################################################################################
# ✨ trans bool into int
new_drop_attn_list = []
if drop_attn_list is not None:
for idx in range(len(drop_attn_list)):
if isinstance(drop_attn_list[idx], bool):
if drop_attn_list[idx] == True:
new_drop_attn_list.append(idx)
elif isinstance(drop_attn_list[idx], int):
new_drop_attn_list.append(drop_attn_list[idx])
new_drop_mlp_list = []
if drop_mlp_list is not None:
for idx in range(len(drop_mlp_list)):
if isinstance(drop_mlp_list[idx], bool):
if drop_mlp_list[idx] == True:
new_drop_mlp_list.append(idx)
elif isinstance(drop_mlp_list[idx], int):
new_drop_mlp_list.append(drop_mlp_list[idx])
#####################################################################################################################
if new_drop_mlp_list:
self.drop_mlp_list = []
for idx in range(self.num_hidden_layers):
self.drop_mlp_list.append(True if idx in new_drop_mlp_list else False)
else:
self.drop_mlp_list = [False] * self.num_hidden_layers
if new_drop_attn_list:
self.drop_attn_list = []
for idx in range(self.num_hidden_layers):
self.drop_attn_list.append(True if idx in new_drop_attn_list else False)
else:
self.drop_attn_list = [False] * self.num_hidden_layers
#####################################################################################################################
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

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}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
{
"content": "<instruct>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
{
"content": "<query>",
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},
{
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}
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"bos_token": {
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},
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},
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"unk_token": {
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"normalized": false,
"rstrip": false,
"single_word": false
}
}

91122
tokenizer.json Normal file

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72
tokenizer_config.json Normal file
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{
"add_bos_token": true,
"add_eos_token": true,
"add_prefix_space": true,
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},
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"special": true
},
"2": {
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"special": true
},
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},
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"special": true
}
},
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"<response>"
],
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}