初始化项目，由ModelHub XC社区提供模型
Model: bluesky333/medphi2 Source: Original Platform
This commit is contained in:
ModelHub XC
35
.gitattributes
vendored
Normal file
35
.gitattributes
vendored
Normal file
@@ -0,0 +1,35 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
9
CODE_OF_CONDUCT.md
Normal file
9
CODE_OF_CONDUCT.md
Normal file
@@ -0,0 +1,9 @@
|
||||
# Microsoft Open Source Code of Conduct
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
||||
|
||||
Resources:
|
||||
|
||||
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
|
||||
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
|
||||
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
|
||||
22
LICENSE
Normal file
22
LICENSE
Normal file
@@ -0,0 +1,22 @@
|
||||
PhyAGI.
|
||||
Copyright (c) Microsoft Corporation.
|
||||
|
||||
MIT License
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
38
NOTICE.md
Normal file
38
NOTICE.md
Normal file
@@ -0,0 +1,38 @@
|
||||
NOTICES AND INFORMATION
|
||||
Do Not Translate or Localize
|
||||
|
||||
This software incorporates material from third parties.
|
||||
|
||||
**Component.** https://github.com/Dao-AILab/flash-attention
|
||||
|
||||
**Open Source License/Copyright Notice.**
|
||||
|
||||
BSD 3-Clause License
|
||||
|
||||
Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file.
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright notice, this
|
||||
list of conditions and the following disclaimer.
|
||||
|
||||
* Redistributions in binary form must reproduce the above copyright notice,
|
||||
this list of conditions and the following disclaimer in the documentation
|
||||
and/or other materials provided with the distribution.
|
||||
|
||||
* Neither the name of the copyright holder nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
||||
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
55
README.md
Normal file
55
README.md
Normal file
@@ -0,0 +1,55 @@
|
||||
---
|
||||
inference: false
|
||||
license: mit
|
||||
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
|
||||
language:
|
||||
- en
|
||||
pipeline_tag: text-generation
|
||||
tags:
|
||||
- nlp
|
||||
- code
|
||||
- biology
|
||||
- medical
|
||||
---
|
||||
|
||||
## Model Summary
|
||||
|
||||
MedPhi-2 is a Phi-2, **2.7 billion** parameters, further trained for the biomedical domain.
|
||||
It was proposed in MedExQA paper.
|
||||
|
||||
<h1> 🧑⚕️ MedExQA </h1>
|
||||
<h3> Medical Question Answering Benchmark with Multiple Explanations </h3>
|
||||
|
||||
<p>
|
||||
📄 <a href="https://arxiv.org/abs/2406.06331" target="_blank">Paper</a> • ⏬ <a href="https://huggingface.co/datasets/bluesky333/MedExQA" target="_blank">Dataset</a> • ⚕️ <a href="https://huggingface.co/bluesky333/medphi2" target="_blank">MedPhi2</a><br>
|
||||
</p>
|
||||
|
||||
|
||||
## Model Details
|
||||
|
||||
### Model Description
|
||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
|
||||
- **Model type:** Clinical LLM (Large Language Model)
|
||||
- **Language(s) (NLP):** English
|
||||
- **License:** [MIT license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE)
|
||||
- **Finetuned from model [optional]:** Phi-2
|
||||
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
```
|
||||
@article{kim2024medexqa,
|
||||
title={MedExQA: Medical Question Answering Benchmark with Multiple Explanations},
|
||||
author={Kim, Yunsoo and Wu, Jinge and Abdulle, Yusuf and Wu, Honghan},
|
||||
journal={arXiv e-prints},
|
||||
pages={arXiv--2406},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
41
SECURITY.md
Normal file
41
SECURITY.md
Normal file
@@ -0,0 +1,41 @@
|
||||
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->
|
||||
|
||||
## Security
|
||||
|
||||
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin).
|
||||
|
||||
If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below.
|
||||
|
||||
## Reporting Security Issues
|
||||
|
||||
**Please do not report security vulnerabilities through public GitHub issues.**
|
||||
|
||||
Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report).
|
||||
|
||||
If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp).
|
||||
|
||||
You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
|
||||
|
||||
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
|
||||
|
||||
* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
|
||||
* Full paths of source file(s) related to the manifestation of the issue
|
||||
* The location of the affected source code (tag/branch/commit or direct URL)
|
||||
* Any special configuration required to reproduce the issue
|
||||
* Step-by-step instructions to reproduce the issue
|
||||
* Proof-of-concept or exploit code (if possible)
|
||||
* Impact of the issue, including how an attacker might exploit the issue
|
||||
|
||||
This information will help us triage your report more quickly.
|
||||
|
||||
If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs.
|
||||
|
||||
## Preferred Languages
|
||||
|
||||
We prefer all communications to be in English.
|
||||
|
||||
## Policy
|
||||
|
||||
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd).
|
||||
|
||||
<!-- END MICROSOFT SECURITY.MD BLOCK -->
|
||||
40
added_tokens.json
Normal file
40
added_tokens.json
Normal file
@@ -0,0 +1,40 @@
|
||||
{
|
||||
"\t\t": 50294,
|
||||
"\t\t\t": 50293,
|
||||
"\t\t\t\t": 50292,
|
||||
"\t\t\t\t\t": 50291,
|
||||
"\t\t\t\t\t\t": 50290,
|
||||
"\t\t\t\t\t\t\t": 50289,
|
||||
"\t\t\t\t\t\t\t\t": 50288,
|
||||
"\t\t\t\t\t\t\t\t\t": 50287,
|
||||
" ": 50286,
|
||||
" ": 50285,
|
||||
" ": 50284,
|
||||
" ": 50283,
|
||||
" ": 50282,
|
||||
" ": 50281,
|
||||
" ": 50280,
|
||||
" ": 50279,
|
||||
" ": 50278,
|
||||
" ": 50277,
|
||||
" ": 50276,
|
||||
" ": 50275,
|
||||
" ": 50274,
|
||||
" ": 50273,
|
||||
" ": 50272,
|
||||
" ": 50271,
|
||||
" ": 50270,
|
||||
" ": 50269,
|
||||
" ": 50268,
|
||||
" ": 50267,
|
||||
" ": 50266,
|
||||
" ": 50265,
|
||||
" ": 50264,
|
||||
" ": 50263,
|
||||
" ": 50262,
|
||||
" ": 50261,
|
||||
" ": 50260,
|
||||
" ": 50259,
|
||||
" ": 50258,
|
||||
" ": 50257
|
||||
}
|
||||
7
all_results.json
Normal file
7
all_results.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"epoch": 3.0,
|
||||
"train_loss": 0.5114809172482763,
|
||||
"train_runtime": 44939.8765,
|
||||
"train_samples_per_second": 16.083,
|
||||
"train_steps_per_second": 0.251
|
||||
}
|
||||
35
config.json
Normal file
35
config.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"_name_or_path": "./model/phi2-knowmed-pretrain",
|
||||
"architectures": [
|
||||
"PhiForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_phi.PhiConfig",
|
||||
"AutoModel": "modeling_phi.PhiForCausalLM",
|
||||
"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
|
||||
},
|
||||
"bos_token_id": null,
|
||||
"embd_pdrop": 0.0,
|
||||
"eos_token_id": null,
|
||||
"hidden_act": "gelu_new",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 10240,
|
||||
"layer_norm_eps": 1e-05,
|
||||
"max_position_embeddings": 2048,
|
||||
"model_type": "phi",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"partial_rotary_factor": 0.4,
|
||||
"qk_layernorm": false,
|
||||
"resid_pdrop": 0.1,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.37.1",
|
||||
"use_cache": false,
|
||||
"vocab_size": 51200
|
||||
}
|
||||
193
configuration_phi.py
Normal file
193
configuration_phi.py
Normal file
@@ -0,0 +1,193 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 Microsoft 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.
|
||||
|
||||
""" Phi model configuration"""
|
||||
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
|
||||
}
|
||||
|
||||
|
||||
class PhiConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
|
||||
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 Phi
|
||||
[microsoft/phi-1](https://huggingface.co/microsoft/phi-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 51200):
|
||||
Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`PhiModel`].
|
||||
hidden_size (`int`, *optional*, defaults to 2048):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 8192):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 24):
|
||||
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`.
|
||||
resid_pdrop (`float`, *optional*, defaults to 0.0):
|
||||
Dropout probability for mlp outputs.
|
||||
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the embeddings.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio after computing the attention scores.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||||
The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
|
||||
tokens.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
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`. Whether to tie weight embeddings or not.
|
||||
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 an 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/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
|
||||
is an experimental feature, subject to breaking API changes in future versions.
|
||||
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
|
||||
Percentage of the query and keys which will have rotary embedding.
|
||||
qk_layernorm (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not to normalize the Queries and Keys after projecting the hidden states.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
Denotes beginning of sequences token id.
|
||||
eos_token_id (`int`, *optional*, defaults to 2):
|
||||
Denotes end of sequences token id.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import PhiModel, PhiConfig
|
||||
|
||||
>>> # Initializing a Phi-1 style configuration
|
||||
>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
|
||||
|
||||
>>> # Initializing a model from the configuration
|
||||
>>> model = PhiModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "phi"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=51200,
|
||||
hidden_size=2048,
|
||||
intermediate_size=8192,
|
||||
num_hidden_layers=24,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
resid_pdrop=0.0,
|
||||
embd_pdrop=0.0,
|
||||
attention_dropout=0.0,
|
||||
hidden_act="gelu_new",
|
||||
max_position_embeddings=2048,
|
||||
initializer_range=0.02,
|
||||
layer_norm_eps=1e-5,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
partial_rotary_factor=0.5,
|
||||
qk_layernorm=False,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attention_dropout = attention_dropout
|
||||
self.hidden_act = hidden_act
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.initializer_range = initializer_range
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self.partial_rotary_factor = partial_rotary_factor
|
||||
self.qk_layernorm = qk_layernorm
|
||||
self._rope_scaling_validation()
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
|
||||
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}")
|
||||
4
generation_config.json
Normal file
4
generation_config.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"transformers_version": "4.37.1"
|
||||
}
|
||||
50001
merges.txt
Normal file
50001
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0d174cb2fdb88f215c3715f29e005e25f10b274e5d56532d3960fcb0ac1ffe30
|
||||
size 4995584848
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0a194dcf9eb2d878921ba43078290483014cacefd734e34084417db9334c0afa
|
||||
size 563833008
|
||||
460
model.safetensors.index.json
Normal file
460
model.safetensors.index.json
Normal file
@@ -0,0 +1,460 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 5559367680
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.bias": "model-00002-of-00002.safetensors",
|
||||
"lm_head.weight": "model-00002-of-00002.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
"model.final_layernorm.bias": "model-00002-of-00002.safetensors",
|
||||
"model.final_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.24.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.25.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.26.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.27.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.30.input_layernorm.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.mlp.fc1.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.mlp.fc1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.mlp.fc2.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.mlp.fc2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.dense.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.dense.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.30.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.input_layernorm.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.mlp.fc1.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.mlp.fc1.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.mlp.fc2.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.mlp.fc2.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.dense.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.dense.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||
"model.layers.31.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.4.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
1369
modeling_phi.py
Normal file
1369
modeling_phi.py
Normal file
File diff suppressed because it is too large
Load Diff
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
328
tokenizer_config.json
Normal file
328
tokenizer_config.json
Normal file
@@ -0,0 +1,328 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"50256": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"50257": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50258": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50259": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50260": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50261": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50262": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50263": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50264": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50265": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50266": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50267": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50268": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50269": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50270": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50271": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50272": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50273": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50274": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50275": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50276": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50277": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50278": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50279": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50280": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50281": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50282": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50283": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50284": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50285": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50286": {
|
||||
"content": " ",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50287": {
|
||||
"content": "\t\t\t\t\t\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50288": {
|
||||
"content": "\t\t\t\t\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50289": {
|
||||
"content": "\t\t\t\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50290": {
|
||||
"content": "\t\t\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50291": {
|
||||
"content": "\t\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50292": {
|
||||
"content": "\t\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50293": {
|
||||
"content": "\t\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"50294": {
|
||||
"content": "\t\t",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 2048,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "CodeGenTokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
50259
vocab.json
Normal file
50259
vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user