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This commit is contained in:
ai-modelscope
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70
README.md
70
README.md
@@ -1,47 +1,35 @@
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|||||||
---
|
---
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||||||
license: Apache License 2.0
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language:
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||||||
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- multilingual
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||||||
#model-type:
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license: mit
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||||||
##如 gpt、phi、llama、chatglm、baichuan 等
|
license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE
|
||||||
#- gpt
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pipeline_tag: text-generation
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||||||
|
tags:
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||||||
#domain:
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- nlp
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||||||
##如 nlp、cv、audio、multi-modal
|
- code
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||||||
#- nlp
|
- mlx
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||||||
|
inference:
|
||||||
#language:
|
parameters:
|
||||||
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
|
temperature: 0.7
|
||||||
#- cn
|
widget:
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||||||
|
- messages:
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||||||
#metrics:
|
- role: user
|
||||||
##如 CIDEr、Blue、ROUGE 等
|
content: Can you provide ways to eat combinations of bananas and dragonfruits?
|
||||||
#- CIDEr
|
|
||||||
|
|
||||||
#tags:
|
|
||||||
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
|
|
||||||
#- pretrained
|
|
||||||
|
|
||||||
#tools:
|
|
||||||
##如 vllm、fastchat、llamacpp、AdaSeq 等
|
|
||||||
#- vllm
|
|
||||||
---
|
---
|
||||||
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
|
|
||||||
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
|
|
||||||
|
|
||||||
SDK下载
|
# mlx-community/Phi-3-medium-128k-instruct-bf16
|
||||||
|
|
||||||
|
The Model [mlx-community/Phi-3-medium-128k-instruct-bf16](https://huggingface.co/mlx-community/Phi-3-medium-128k-instruct-bf16) was converted to MLX format from [microsoft/Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) using mlx-lm version **0.18.1**.
|
||||||
|
|
||||||
|
## Use with mlx
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
#安装ModelScope
|
pip install mlx-lm
|
||||||
pip install modelscope
|
|
||||||
```
|
|
||||||
```python
|
|
||||||
#SDK模型下载
|
|
||||||
from modelscope import snapshot_download
|
|
||||||
model_dir = snapshot_download('mlx-community/Phi-3-medium-128k-instruct-bf16')
|
|
||||||
```
|
|
||||||
Git下载
|
|
||||||
```
|
|
||||||
#Git模型下载
|
|
||||||
git clone https://www.modelscope.cn/mlx-community/Phi-3-medium-128k-instruct-bf16.git
|
|
||||||
```
|
```
|
||||||
|
|
||||||
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
|
```python
|
||||||
|
from mlx_lm import load, generate
|
||||||
|
|
||||||
|
model, tokenizer = load("mlx-community/Phi-3-medium-128k-instruct-bf16")
|
||||||
|
response = generate(model, tokenizer, prompt="hello", verbose=True)
|
||||||
|
```
|
||||||
|
|||||||
13
added_tokens.json
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13
added_tokens.json
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|||||||
|
{
|
||||||
|
"<|assistant|>": 32001,
|
||||||
|
"<|endoftext|>": 32000,
|
||||||
|
"<|end|>": 32007,
|
||||||
|
"<|placeholder1|>": 32002,
|
||||||
|
"<|placeholder2|>": 32003,
|
||||||
|
"<|placeholder3|>": 32004,
|
||||||
|
"<|placeholder4|>": 32005,
|
||||||
|
"<|placeholder5|>": 32008,
|
||||||
|
"<|placeholder6|>": 32009,
|
||||||
|
"<|system|>": 32006,
|
||||||
|
"<|user|>": 32010
|
||||||
|
}
|
||||||
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config.json
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config.json
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@@ -0,0 +1,169 @@
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|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Phi3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"auto_map": {
|
||||||
|
"AutoConfig": "configuration_phi3.Phi3Config",
|
||||||
|
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
|
||||||
|
},
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"embd_pdrop": 0.0,
|
||||||
|
"eos_token_id": 32000,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 5120,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 17920,
|
||||||
|
"max_position_embeddings": 131072,
|
||||||
|
"model_type": "phi3",
|
||||||
|
"num_attention_heads": 40,
|
||||||
|
"num_hidden_layers": 40,
|
||||||
|
"num_key_value_heads": 10,
|
||||||
|
"original_max_position_embeddings": 4096,
|
||||||
|
"pad_token_id": null,
|
||||||
|
"resid_pdrop": 0.0,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_scaling": {
|
||||||
|
"long_factor": [
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.25,
|
||||||
|
1.25,
|
||||||
|
1.5,
|
||||||
|
2.0,
|
||||||
|
2.75,
|
||||||
|
5.75,
|
||||||
|
5.75,
|
||||||
|
6.5,
|
||||||
|
9.25,
|
||||||
|
11.0,
|
||||||
|
13.25,
|
||||||
|
19.25,
|
||||||
|
19.75,
|
||||||
|
19.75,
|
||||||
|
21.25,
|
||||||
|
21.5,
|
||||||
|
26.5,
|
||||||
|
30.0,
|
||||||
|
33.75,
|
||||||
|
35.25,
|
||||||
|
38.5,
|
||||||
|
42.0,
|
||||||
|
42.25,
|
||||||
|
46.0,
|
||||||
|
47.0,
|
||||||
|
50.0,
|
||||||
|
50.5,
|
||||||
|
51.0,
|
||||||
|
52.0,
|
||||||
|
52.75,
|
||||||
|
53.75,
|
||||||
|
54.75,
|
||||||
|
57.0,
|
||||||
|
57.25,
|
||||||
|
58.5,
|
||||||
|
59.25,
|
||||||
|
59.5,
|
||||||
|
62.0,
|
||||||
|
62.5,
|
||||||
|
62.75,
|
||||||
|
63.25,
|
||||||
|
63.25,
|
||||||
|
63.25,
|
||||||
|
63.75,
|
||||||
|
64.0,
|
||||||
|
64.0,
|
||||||
|
64.25,
|
||||||
|
64.5,
|
||||||
|
64.5,
|
||||||
|
65.0,
|
||||||
|
65.0
|
||||||
|
],
|
||||||
|
"short_factor": [
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.0,
|
||||||
|
1.01,
|
||||||
|
1.02,
|
||||||
|
1.02,
|
||||||
|
1.04,
|
||||||
|
1.04,
|
||||||
|
1.07,
|
||||||
|
1.07,
|
||||||
|
1.1,
|
||||||
|
1.3000000000000003,
|
||||||
|
1.3000000000000003,
|
||||||
|
1.5000000000000004,
|
||||||
|
1.5700000000000005,
|
||||||
|
1.9000000000000008,
|
||||||
|
2.3100000000000014,
|
||||||
|
2.759999999999992,
|
||||||
|
3.3899999999999784,
|
||||||
|
3.9399999999999666,
|
||||||
|
4.009999999999965,
|
||||||
|
4.289999999999959,
|
||||||
|
4.349999999999958,
|
||||||
|
5.349999999999937,
|
||||||
|
6.659999999999909,
|
||||||
|
7.029999999999901,
|
||||||
|
7.51999999999989,
|
||||||
|
8.00999999999988,
|
||||||
|
8.249999999999876,
|
||||||
|
8.279999999999875,
|
||||||
|
9.629999999999846,
|
||||||
|
9.89999999999984,
|
||||||
|
10.589999999999826,
|
||||||
|
11.049999999999816,
|
||||||
|
11.7899999999998,
|
||||||
|
12.189999999999792,
|
||||||
|
12.889999999999777,
|
||||||
|
13.129999999999772,
|
||||||
|
13.16999999999977,
|
||||||
|
13.20999999999977,
|
||||||
|
13.479999999999764,
|
||||||
|
13.539999999999763,
|
||||||
|
13.779999999999758,
|
||||||
|
13.929999999999755,
|
||||||
|
14.429999999999744,
|
||||||
|
14.759999999999737,
|
||||||
|
15.149999999999729,
|
||||||
|
15.419999999999723,
|
||||||
|
15.53999999999972,
|
||||||
|
15.659999999999718,
|
||||||
|
15.749999999999716,
|
||||||
|
15.759999999999716,
|
||||||
|
15.799999999999715,
|
||||||
|
16.05999999999971,
|
||||||
|
16.079999999999714,
|
||||||
|
16.11999999999972,
|
||||||
|
16.11999999999972,
|
||||||
|
16.18999999999973,
|
||||||
|
16.31999999999975,
|
||||||
|
16.539999999999786,
|
||||||
|
16.799999999999827
|
||||||
|
],
|
||||||
|
"type": "su"
|
||||||
|
},
|
||||||
|
"rope_theta": 10000.0,
|
||||||
|
"sliding_window": 131072,
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.39.3",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 32064
|
||||||
|
}
|
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1
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|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
213
configuration_phi3.py
Normal file
213
configuration_phi3.py
Normal file
@@ -0,0 +1,213 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2024 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-3 model configuration"""
|
||||||
|
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||||
|
"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
|
||||||
|
"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class Phi3Config(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
|
||||||
|
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
|
||||||
|
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
|
||||||
|
|
||||||
|
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 32064):
|
||||||
|
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
|
||||||
|
`inputs_ids` passed when calling [`Phi3Model`].
|
||||||
|
hidden_size (`int`, *optional*, defaults to 3072):
|
||||||
|
Dimension of the hidden representations.
|
||||||
|
intermediate_size (`int`, *optional*, defaults to 8192):
|
||||||
|
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`.
|
||||||
|
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 `"silu"`):
|
||||||
|
The non-linear activation function (function or string) in the decoder.
|
||||||
|
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||||
|
The maximum sequence length that this model might ever be used with.
|
||||||
|
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||||
|
The maximum sequence length that this model was trained with. This is used to determine the size of the
|
||||||
|
original RoPE embeddings when using long scaling.
|
||||||
|
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-05):
|
||||||
|
The epsilon value used for the RMSNorm.
|
||||||
|
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*):
|
||||||
|
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
|
||||||
|
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
|
||||||
|
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
|
||||||
|
divided by the number of attention heads divided by 2.
|
||||||
|
bos_token_id (`int`, *optional*, defaults to 1):
|
||||||
|
The id of the "beginning-of-sequence" token.
|
||||||
|
eos_token_id (`int`, *optional*, defaults to 32000):
|
||||||
|
The id of the "end-of-sequence" token.
|
||||||
|
pad_token_id (`int`, *optional*, defaults to 32000):
|
||||||
|
The id of the padding token.
|
||||||
|
sliding_window (`int`, *optional*):
|
||||||
|
Sliding window attention window size. If `None`, no sliding window is applied.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import Phi3Model, Phi3Config
|
||||||
|
|
||||||
|
>>> # Initializing a Phi-3 style configuration
|
||||||
|
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
|
||||||
|
|
||||||
|
>>> # Initializing a model from the configuration
|
||||||
|
>>> model = Phi3Model(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
|
||||||
|
model_type = "phi3"
|
||||||
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size=32064,
|
||||||
|
hidden_size=3072,
|
||||||
|
intermediate_size=8192,
|
||||||
|
num_hidden_layers=32,
|
||||||
|
num_attention_heads=32,
|
||||||
|
num_key_value_heads=None,
|
||||||
|
resid_pdrop=0.0,
|
||||||
|
embd_pdrop=0.0,
|
||||||
|
attention_dropout=0.0,
|
||||||
|
hidden_act="silu",
|
||||||
|
max_position_embeddings=4096,
|
||||||
|
original_max_position_embeddings=4096,
|
||||||
|
initializer_range=0.02,
|
||||||
|
rms_norm_eps=1e-5,
|
||||||
|
use_cache=True,
|
||||||
|
tie_word_embeddings=False,
|
||||||
|
rope_theta=10000.0,
|
||||||
|
rope_scaling=None,
|
||||||
|
bos_token_id=1,
|
||||||
|
eos_token_id=32000,
|
||||||
|
pad_token_id=32000,
|
||||||
|
sliding_window=None,
|
||||||
|
**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.original_max_position_embeddings = original_max_position_embeddings
|
||||||
|
self.initializer_range = initializer_range
|
||||||
|
self.rms_norm_eps = rms_norm_eps
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.rope_theta = rope_theta
|
||||||
|
self.rope_scaling = rope_scaling
|
||||||
|
self._rope_scaling_validation()
|
||||||
|
self.sliding_window = sliding_window
|
||||||
|
|
||||||
|
super().__init__(
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
pad_token_id=pad_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) != 3:
|
||||||
|
raise ValueError(
|
||||||
|
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
|
||||||
|
f"got {self.rope_scaling}"
|
||||||
|
)
|
||||||
|
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||||
|
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
|
||||||
|
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
|
||||||
|
if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
|
||||||
|
raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
|
||||||
|
if not (
|
||||||
|
isinstance(rope_scaling_short_factor, list)
|
||||||
|
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
|
||||||
|
)
|
||||||
|
if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
|
||||||
|
)
|
||||||
|
if not (
|
||||||
|
isinstance(rope_scaling_long_factor, list)
|
||||||
|
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
|
||||||
|
):
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
|
||||||
|
)
|
||||||
|
if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
|
||||||
|
)
|
||||||
3
model-00001-of-00006.safetensors
Normal file
3
model-00001-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3f4aa89b8f49edf759086f3ba03ceb7d13c8f952c037eabe227abe6cbb34be6d
|
||||||
|
size 5230576771
|
||||||
3
model-00002-of-00006.safetensors
Normal file
3
model-00002-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:8c09069ab0098038d30331c205283accde26d34bf6c8e189540bead8770e7de3
|
||||||
|
size 5321692436
|
||||||
3
model-00003-of-00006.safetensors
Normal file
3
model-00003-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2c390cf11da5855eb576771577f82b7da818595a1de9503c709ad22fbf2ab286
|
||||||
|
size 5269243073
|
||||||
3
model-00004-of-00006.safetensors
Normal file
3
model-00004-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:622c046134c41dff94e3a6434df60ec195bd4a93b2987a22651937c8fc7c57ba
|
||||||
|
size 5085762981
|
||||||
3
model-00005-of-00006.safetensors
Normal file
3
model-00005-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2d3d55fa6f139fbe7375dcc041276b087652d12030ba263edda3af3fc7e5743f
|
||||||
|
size 5321692458
|
||||||
3
model-00006-of-00006.safetensors
Normal file
3
model-00006-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:7bca60d2cda820e99f9cc7c2273da45cae1faf94eaa4de1af6dc2bafe999338c
|
||||||
|
size 1691536941
|
||||||
250
model.safetensors.index.json
Normal file
250
model.safetensors.index.json
Normal file
@@ -0,0 +1,250 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 27920476160
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"lm_head.weight": "model-00006-of-00006.safetensors",
|
||||||
|
"model.embed_tokens.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_up_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.0.self_attn.qkv_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_up_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.1.self_attn.qkv_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.10.self_attn.qkv_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.11.self_attn.qkv_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.12.self_attn.qkv_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.13.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.13.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.13.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.13.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.13.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
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"model.layers.5.mlp.gate_up_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.5.self_attn.qkv_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.mlp.gate_up_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.6.self_attn.qkv_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.7.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.7.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.7.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.7.self_attn.qkv_proj.weight": "model-00001-of-00006.safetensors",
|
||||||
|
"model.layers.8.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.8.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.8.self_attn.qkv_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.mlp.gate_up_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.layers.9.self_attn.qkv_proj.weight": "model-00002-of-00006.safetensors",
|
||||||
|
"model.norm.weight": "model-00006-of-00006.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
1606
modeling_phi3.py
Normal file
1606
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
214
sample_finetune.py
Normal file
214
sample_finetune.py
Normal file
@@ -0,0 +1,214 @@
|
|||||||
|
import sys
|
||||||
|
import logging
|
||||||
|
|
||||||
|
import datasets
|
||||||
|
from datasets import load_dataset
|
||||||
|
from peft import LoraConfig
|
||||||
|
import torch
|
||||||
|
import transformers
|
||||||
|
from trl import SFTTrainer
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
|
||||||
|
|
||||||
|
"""
|
||||||
|
A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
|
||||||
|
a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
|
||||||
|
This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
|
||||||
|
script can be run on V100 or later generation GPUs. Here are some suggestions on
|
||||||
|
futher reducing memory consumption:
|
||||||
|
- reduce batch size
|
||||||
|
- decrease lora dimension
|
||||||
|
- restrict lora target modules
|
||||||
|
Please follow these steps to run the script:
|
||||||
|
1. Install dependencies:
|
||||||
|
conda install -c conda-forge accelerate
|
||||||
|
pip3 install -i https://pypi.org/simple/ bitsandbytes
|
||||||
|
pip3 install peft transformers trl datasets
|
||||||
|
pip3 install deepspeed
|
||||||
|
2. Setup accelerate and deepspeed config based on the machine used:
|
||||||
|
accelerate config
|
||||||
|
Here is a sample config for deepspeed zero3:
|
||||||
|
compute_environment: LOCAL_MACHINE
|
||||||
|
debug: false
|
||||||
|
deepspeed_config:
|
||||||
|
gradient_accumulation_steps: 1
|
||||||
|
offload_optimizer_device: none
|
||||||
|
offload_param_device: none
|
||||||
|
zero3_init_flag: true
|
||||||
|
zero3_save_16bit_model: true
|
||||||
|
zero_stage: 3
|
||||||
|
distributed_type: DEEPSPEED
|
||||||
|
downcast_bf16: 'no'
|
||||||
|
enable_cpu_affinity: false
|
||||||
|
machine_rank: 0
|
||||||
|
main_training_function: main
|
||||||
|
mixed_precision: bf16
|
||||||
|
num_machines: 1
|
||||||
|
num_processes: 4
|
||||||
|
rdzv_backend: static
|
||||||
|
same_network: true
|
||||||
|
tpu_env: []
|
||||||
|
tpu_use_cluster: false
|
||||||
|
tpu_use_sudo: false
|
||||||
|
use_cpu: false
|
||||||
|
3. check accelerate config:
|
||||||
|
accelerate env
|
||||||
|
4. Run the code:
|
||||||
|
accelerate launch sample_finetune.py
|
||||||
|
"""
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
###################
|
||||||
|
# Hyper-parameters
|
||||||
|
###################
|
||||||
|
training_config = {
|
||||||
|
"bf16": True,
|
||||||
|
"do_eval": False,
|
||||||
|
"learning_rate": 5.0e-06,
|
||||||
|
"log_level": "info",
|
||||||
|
"logging_steps": 20,
|
||||||
|
"logging_strategy": "steps",
|
||||||
|
"lr_scheduler_type": "cosine",
|
||||||
|
"num_train_epochs": 1,
|
||||||
|
"max_steps": -1,
|
||||||
|
"output_dir": "./checkpoint_dir",
|
||||||
|
"overwrite_output_dir": True,
|
||||||
|
"per_device_eval_batch_size": 4,
|
||||||
|
"per_device_train_batch_size": 4,
|
||||||
|
"remove_unused_columns": True,
|
||||||
|
"save_steps": 100,
|
||||||
|
"save_total_limit": 1,
|
||||||
|
"seed": 0,
|
||||||
|
"gradient_checkpointing": True,
|
||||||
|
"gradient_checkpointing_kwargs":{"use_reentrant": False},
|
||||||
|
"gradient_accumulation_steps": 1,
|
||||||
|
"warmup_ratio": 0.2,
|
||||||
|
}
|
||||||
|
|
||||||
|
peft_config = {
|
||||||
|
"r": 16,
|
||||||
|
"lora_alpha": 32,
|
||||||
|
"lora_dropout": 0.05,
|
||||||
|
"bias": "none",
|
||||||
|
"task_type": "CAUSAL_LM",
|
||||||
|
"target_modules": "all-linear",
|
||||||
|
"modules_to_save": None,
|
||||||
|
}
|
||||||
|
train_conf = TrainingArguments(**training_config)
|
||||||
|
peft_conf = LoraConfig(**peft_config)
|
||||||
|
|
||||||
|
|
||||||
|
###############
|
||||||
|
# Setup logging
|
||||||
|
###############
|
||||||
|
logging.basicConfig(
|
||||||
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||||
|
datefmt="%Y-%m-%d %H:%M:%S",
|
||||||
|
handlers=[logging.StreamHandler(sys.stdout)],
|
||||||
|
)
|
||||||
|
log_level = train_conf.get_process_log_level()
|
||||||
|
logger.setLevel(log_level)
|
||||||
|
datasets.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.enable_default_handler()
|
||||||
|
transformers.utils.logging.enable_explicit_format()
|
||||||
|
|
||||||
|
# Log on each process a small summary
|
||||||
|
logger.warning(
|
||||||
|
f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
|
||||||
|
+ f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
|
||||||
|
)
|
||||||
|
logger.info(f"Training/evaluation parameters {train_conf}")
|
||||||
|
logger.info(f"PEFT parameters {peft_conf}")
|
||||||
|
|
||||||
|
|
||||||
|
################
|
||||||
|
# Modle Loading
|
||||||
|
################
|
||||||
|
checkpoint_path = "microsoft/Phi-3-medium-4k-instruct"
|
||||||
|
# checkpoint_path = "microsoft/Phi-3-medium-128k-instruct"
|
||||||
|
model_kwargs = dict(
|
||||||
|
use_cache=False,
|
||||||
|
trust_remote_code=True,
|
||||||
|
attn_implementation="flash_attention_2", # loading the model with flash-attenstion support
|
||||||
|
torch_dtype=torch.bfloat16,
|
||||||
|
device_map=None
|
||||||
|
)
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
|
||||||
|
tokenizer.model_max_length = 2048
|
||||||
|
tokenizer.pad_token = tokenizer.unk_token # use unk rather than eos token to prevent endless generation
|
||||||
|
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
|
||||||
|
tokenizer.padding_side = 'right'
|
||||||
|
|
||||||
|
|
||||||
|
##################
|
||||||
|
# Data Processing
|
||||||
|
##################
|
||||||
|
def apply_chat_template(
|
||||||
|
example,
|
||||||
|
tokenizer,
|
||||||
|
):
|
||||||
|
messages = example["messages"]
|
||||||
|
example["text"] = tokenizer.apply_chat_template(
|
||||||
|
messages, tokenize=False, add_generation_prompt=False)
|
||||||
|
return example
|
||||||
|
|
||||||
|
raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
|
||||||
|
train_dataset = raw_dataset["train_sft"]
|
||||||
|
test_dataset = raw_dataset["test_sft"]
|
||||||
|
column_names = list(train_dataset.features)
|
||||||
|
|
||||||
|
processed_train_dataset = train_dataset.map(
|
||||||
|
apply_chat_template,
|
||||||
|
fn_kwargs={"tokenizer": tokenizer},
|
||||||
|
num_proc=10,
|
||||||
|
remove_columns=column_names,
|
||||||
|
desc="Applying chat template to train_sft",
|
||||||
|
)
|
||||||
|
|
||||||
|
processed_test_dataset = test_dataset.map(
|
||||||
|
apply_chat_template,
|
||||||
|
fn_kwargs={"tokenizer": tokenizer},
|
||||||
|
num_proc=10,
|
||||||
|
remove_columns=column_names,
|
||||||
|
desc="Applying chat template to test_sft",
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Training
|
||||||
|
###########
|
||||||
|
trainer = SFTTrainer(
|
||||||
|
model=model,
|
||||||
|
args=train_conf,
|
||||||
|
peft_config=peft_conf,
|
||||||
|
train_dataset=processed_train_dataset,
|
||||||
|
eval_dataset=processed_test_dataset,
|
||||||
|
max_seq_length=2048,
|
||||||
|
dataset_text_field="text",
|
||||||
|
tokenizer=tokenizer,
|
||||||
|
packing=True
|
||||||
|
)
|
||||||
|
train_result = trainer.train()
|
||||||
|
metrics = train_result.metrics
|
||||||
|
trainer.log_metrics("train", metrics)
|
||||||
|
trainer.save_metrics("train", metrics)
|
||||||
|
trainer.save_state()
|
||||||
|
|
||||||
|
|
||||||
|
#############
|
||||||
|
# Evaluation
|
||||||
|
#############
|
||||||
|
tokenizer.padding_side = 'left'
|
||||||
|
metrics = trainer.evaluate()
|
||||||
|
metrics["eval_samples"] = len(processed_test_dataset)
|
||||||
|
trainer.log_metrics("eval", metrics)
|
||||||
|
trainer.save_metrics("eval", metrics)
|
||||||
|
|
||||||
|
|
||||||
|
# ############
|
||||||
|
# # Save model
|
||||||
|
# ############
|
||||||
|
trainer.save_model(train_conf.output_dir)
|
||||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<s>",
|
||||||
|
"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": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
93463
tokenizer.json
Normal file
93463
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
131
tokenizer_config.json
Normal file
131
tokenizer_config.json
Normal file
@@ -0,0 +1,131 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"add_prefix_space": null,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"32000": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32001": {
|
||||||
|
"content": "<|assistant|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32002": {
|
||||||
|
"content": "<|placeholder1|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32003": {
|
||||||
|
"content": "<|placeholder2|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32004": {
|
||||||
|
"content": "<|placeholder3|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32005": {
|
||||||
|
"content": "<|placeholder4|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32006": {
|
||||||
|
"content": "<|system|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32007": {
|
||||||
|
"content": "<|end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32008": {
|
||||||
|
"content": "<|placeholder5|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32009": {
|
||||||
|
"content": "<|placeholder6|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32010": {
|
||||||
|
"content": "<|user|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": true,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"chat_template": "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"legacy": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": false
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user