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---
license: other
---
![Aquila_logo](./log.jpeg)
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/Aquila2-7B/blob/main/README_zh.md">简体中文</a> |
<p>
</h4>
We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k**
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels.
## Updates 2024.6.6
We have updated the basic language model **Aquila2-7B**, which has the following advantages compared to the previous model:
* Replaced tokenizer with higher compression ratio:
| Tokenizer | Size | Zh | En | Code | Math | Average |
|-----------|-------|--------------------------|--------|-------|-------|---------|
| Aquila2-original | 100k | **4.70** | 4.42 | 3.20 | 3.77 | 4.02 |
| Qwen1.5 | 151k | 4.27 | 4.51 | 3.62 | 3.35 | 3.94 |
| Llama3 | 128k | 3.45 | **4.61** | 3.77 | **3.88** | 3.93 |
| Aquila2-new | 143k | 4.60 | **4.61** | **3.78** | **3.88** | **4.22** |
* The maximum processing length supported by the model has increased from 2048 to 8192
## Quick Start Aquila2-7B
### 1. Inference
Aquila2-7B is a base model that can be used for continuation.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
device= "cuda:0"
# Model Name
model_name = 'BAAI/Aquila2-7B'
# load model and tokenizer
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True,
# quantization_config=quantization_config # Uncomment this one for 4-bit quantization
)
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model.eval()
model.to(device)
# Example
text = "The meaning of life is"
tokens = tokenizer.encode_plus(text)['input_ids']
tokens = torch.tensor(tokens)[None,].to(device)
with torch.no_grad():
out = model.generate(tokens, do_sample=False, max_length=128, eos_token_id=tokenizer.eos_token_id)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## License
Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)

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---
license: other
---
![Aquila_logo](./log.jpeg)
<h4 align="center">
<p>
<a href="https://huggingface.co/BAAI/Aquila2-7B/blob/main/README.md">English</a> |
<b>简体中文</b> |
<p>
</h4>
# 悟道·天鹰（Aquila2）
我们开源了我们的 **Aquila2** 系列，现在包括基础语言模型 **Aquila2-7B** 和 **Aquila2-34B** ，对话模型 **AquilaChat2-7B** 和 **AquilaChat2-34B**，长文本对话模型**AquilaChat2-7B-16k** 和 **AquilaChat2-34B-16k**
悟道 · 天鹰 Aquila 模型的更多细节将在官方技术报告中呈现。请关注官方渠道更新。
## 更新/Updates 2024.6.6
我们更新了基础语言模型 **Aquila2-7B**，该模型是基于原版模型经过继续训练得到的，和之前的模型相比，新的模型具备以下优势：
* 更换了具备更大压缩率的tokenizer，不同tokenizer的压缩率对比如下面表格：
| Tokenizer | Size | Zh | En | Code | Math | Average |
|-----------|-------|--------------------------|--------|-------|-------|---------|
| Aquila2-original | 100k | **4.70** | 4.42 | 3.20 | 3.77 | 4.02 |
| Qwen1.5 | 151k | 4.27 | 4.51 | 3.62 | 3.35 | 3.94 |
| Llama3 | 128k | 3.45 | **4.61** | 3.77 | **3.88** | 3.93 |
| Aquila2-new | 143k | 4.60 | **4.61** | **3.78** | **3.88** | **4.22** |
* 模型支持的最大处理长度从2048增加至8192
## 快速开始使用 Aquila-7B
## 使用方式/How to use
### 1. 推理/Inference
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
device= "cuda:0"
# 模型名称/Model Name
model_name = 'BAAI/Aquila2-7B'
# 加载模型以及tokenizer
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True,
# quantization_config=quantization_config # Uncomment this one for 4-bit quantization
)
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model.eval()
model.to(device)
# 对话测试样例/Example
text = "生命的意义是"
tokens = tokenizer.encode_plus(text)['input_ids']
tokens = torch.tensor(tokens)[None,].to(device)
with torch.no_grad():
out = model.generate(tokens, do_sample=False, max_length=128, eos_token_id=tokenizer.eos_token_id)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## 证书/License
`Aquila2系列开源模型使用 [智源Aquila系列模型许可协议](https://huggingface.co/BAAI/Aquila2-7B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf)

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{
"architectures": [
"AquilaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_aquila.AquilaConfig",
"AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"
},
"bos_token_id": 143717,
"eos_token_id": 143718,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 8192,
"model_type": "aquila",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 1000000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.39.3",
"use_cache": true,
"vocab_size": 143973
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
# Most of the source code is adapted from Llama's source code
""" Aquila model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
AQUILA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class AquilaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AquilaModel`]. It is used to instantiate an Aquila
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 Aquila-7B.
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 143973):
Vocabulary size of the Aquila model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`AquilaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
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
`num_attention_heads`.
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 8192):
The maximum sequence length that this model might ever be used with.
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*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import AquilaModel, AquilaConfig
>>> # Initializing a Aquila aquila-7b style configuration
>>> configuration = AquilaConfig()
>>> # Initializing a model from the aquila-7b style configuration
>>> model = AquilaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "aquila"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=143973,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**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
# 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.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attention_bias = attention_bias
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,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

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{
"_from_model_config": true,
"bos_token_id": 143717,
"eos_token_id": 143718,
"transformers_version": "4.39.3"
}

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{
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