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Cherrytest
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.gitattributes
vendored
2
.gitattributes
vendored
@@ -45,3 +45,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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||||
*.wasm filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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74
README.md
74
README.md
@@ -1,47 +1,39 @@
|
||||
---
|
||||
license: Apache License 2.0
|
||||
|
||||
#model-type:
|
||||
##如 gpt、phi、llama、chatglm、baichuan 等
|
||||
#- gpt
|
||||
|
||||
#domain:
|
||||
##如 nlp、cv、audio、multi-modal
|
||||
#- nlp
|
||||
|
||||
#language:
|
||||
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
|
||||
#- cn
|
||||
|
||||
#metrics:
|
||||
##如 CIDEr、Blue、ROUGE 等
|
||||
#- 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
|
||||
license: apache-2.0
|
||||
language:
|
||||
- zh
|
||||
- en
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||||
pipeline_tag: text-generation
|
||||
library_name: mlx
|
||||
base_model: openbmb/MiniCPM4.1-8B
|
||||
tags:
|
||||
- mlx
|
||||
---
|
||||
### 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/MiniCPM4.1-8B-bf16
|
||||
|
||||
This model [mlx-community/MiniCPM4.1-8B-bf16](https://huggingface.co/mlx-community/MiniCPM4.1-8B-bf16) was
|
||||
converted to MLX format from [openbmb/MiniCPM4.1-8B](https://huggingface.co/openbmb/MiniCPM4.1-8B)
|
||||
using mlx-lm version **0.26.0**.
|
||||
|
||||
## Use with mlx
|
||||
|
||||
```bash
|
||||
#安装ModelScope
|
||||
pip install modelscope
|
||||
```
|
||||
```python
|
||||
#SDK模型下载
|
||||
from modelscope import snapshot_download
|
||||
model_dir = snapshot_download('mlx-community/MiniCPM4.1-8B-bf16')
|
||||
```
|
||||
Git下载
|
||||
```
|
||||
#Git模型下载
|
||||
git clone https://www.modelscope.cn/mlx-community/MiniCPM4.1-8B-bf16.git
|
||||
pip install mlx-lm
|
||||
```
|
||||
|
||||
<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/MiniCPM4.1-8B-bf16")
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||||
|
||||
prompt = "hello"
|
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|
||||
if tokenizer.chat_template is not None:
|
||||
messages = [{"role": "user", "content": prompt}]
|
||||
prompt = tokenizer.apply_chat_template(
|
||||
messages, add_generation_prompt=True
|
||||
)
|
||||
|
||||
response = generate(model, tokenizer, prompt=prompt, verbose=True)
|
||||
```
|
||||
|
||||
10
added_tokens.json
Normal file
10
added_tokens.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"<|execute_end|>": 73444,
|
||||
"<|execute_start|>": 73443,
|
||||
"<|fim_middle|>": 73446,
|
||||
"<|fim_prefix|>": 73445,
|
||||
"<|fim_suffix|>": 73447,
|
||||
"<|im_end|>": 73440,
|
||||
"<|im_start|>": 73441,
|
||||
"<|tool_call|>": 73442
|
||||
}
|
||||
7
chat_template.jinja
Normal file
7
chat_template.jinja
Normal file
@@ -0,0 +1,7 @@
|
||||
{% for message in messages %}{{'<|im_start|>' + message['role'] + '
|
||||
' + message['content'] + '<|im_end|>' + '
|
||||
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
|
||||
' }}{% if enable_thinking is defined and enable_thinking is false %}{{ '<think>
|
||||
|
||||
</think>
|
||||
' }}{% endif %}{% endif %}
|
||||
174
config.json
Normal file
174
config.json
Normal file
@@ -0,0 +1,174 @@
|
||||
{
|
||||
"architectures": [
|
||||
"MiniCPMForCausalLM"
|
||||
],
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
|
||||
"AutoModel": "modeling_minicpm.MiniCPMModel",
|
||||
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||||
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||||
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
|
||||
},
|
||||
"bos_token_id": 1,
|
||||
"dim_model_base": 256,
|
||||
"eos_token_id": [
|
||||
2,
|
||||
73440
|
||||
],
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.1,
|
||||
"intermediate_size": 16384,
|
||||
"max_position_embeddings": 65536,
|
||||
"model_type": "minicpm",
|
||||
"mup_denominator": 32,
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"rope_type": "longrope",
|
||||
"long_factor": [
|
||||
0.9982316082870437,
|
||||
1.033048153422584,
|
||||
1.0749920956484724,
|
||||
1.1255096879436193,
|
||||
1.1863348602111476,
|
||||
1.259543828902579,
|
||||
1.3476188888731149,
|
||||
1.4535223827776373,
|
||||
1.5807816745852985,
|
||||
1.7335856049489526,
|
||||
1.9168922912975785,
|
||||
2.1365471404135326,
|
||||
2.3994084200118646,
|
||||
2.713475511863602,
|
||||
3.0880118452194134,
|
||||
3.533650295140154,
|
||||
4.062463396503134,
|
||||
4.687974098908333,
|
||||
5.425075306704039,
|
||||
6.289818967956352,
|
||||
7.29902962722721,
|
||||
8.469695779093664,
|
||||
9.81809877306655,
|
||||
11.358657902065282,
|
||||
13.102505860712087,
|
||||
15.055862949967128,
|
||||
17.218348131364184,
|
||||
19.581439255386453,
|
||||
22.127353314656723,
|
||||
24.828633849376587,
|
||||
27.6486820771775,
|
||||
30.54334096108829,
|
||||
33.46345345363812,
|
||||
36.358112337548896,
|
||||
39.17816056534983,
|
||||
41.879441100069684,
|
||||
44.425355159339965,
|
||||
46.78844628336223,
|
||||
48.95093146475928,
|
||||
50.90428855401433,
|
||||
52.648136512661125,
|
||||
54.18869564165987,
|
||||
55.537098635632745,
|
||||
56.7077647874992,
|
||||
57.71697544677006,
|
||||
58.58171910802236,
|
||||
59.31882031581807,
|
||||
59.94433101822328,
|
||||
60.47314411958625,
|
||||
60.918782569507,
|
||||
61.29331890286281,
|
||||
61.60738599471455,
|
||||
61.87024727431288,
|
||||
62.089902123428836,
|
||||
62.27320880977746,
|
||||
62.42601274014111,
|
||||
62.55327203194878,
|
||||
62.65917552585329,
|
||||
62.74725058582382,
|
||||
62.82045955451526,
|
||||
62.88128472678279,
|
||||
62.931802319077946,
|
||||
62.97374626130382,
|
||||
63.008562806439365
|
||||
],
|
||||
"short_factor": [
|
||||
0.9982316082870437,
|
||||
1.033048153422584,
|
||||
1.0749920956484724,
|
||||
1.1255096879436193,
|
||||
1.1863348602111476,
|
||||
1.259543828902579,
|
||||
1.3476188888731149,
|
||||
1.4535223827776373,
|
||||
1.5807816745852985,
|
||||
1.7335856049489526,
|
||||
1.9168922912975785,
|
||||
2.1365471404135326,
|
||||
2.3994084200118646,
|
||||
2.713475511863602,
|
||||
3.0880118452194134,
|
||||
3.533650295140154,
|
||||
4.062463396503134,
|
||||
4.687974098908333,
|
||||
5.425075306704039,
|
||||
6.289818967956352,
|
||||
7.29902962722721,
|
||||
8.469695779093664,
|
||||
9.81809877306655,
|
||||
11.358657902065282,
|
||||
13.102505860712087,
|
||||
15.055862949967128,
|
||||
17.218348131364184,
|
||||
19.581439255386453,
|
||||
22.127353314656723,
|
||||
24.828633849376587,
|
||||
27.6486820771775,
|
||||
30.54334096108829,
|
||||
33.46345345363812,
|
||||
36.358112337548896,
|
||||
39.17816056534983,
|
||||
41.879441100069684,
|
||||
44.425355159339965,
|
||||
46.78844628336223,
|
||||
48.95093146475928,
|
||||
50.90428855401433,
|
||||
52.648136512661125,
|
||||
54.18869564165987,
|
||||
55.537098635632745,
|
||||
56.7077647874992,
|
||||
57.71697544677006,
|
||||
58.58171910802236,
|
||||
59.31882031581807,
|
||||
59.94433101822328,
|
||||
60.47314411958625,
|
||||
60.918782569507,
|
||||
61.29331890286281,
|
||||
61.60738599471455,
|
||||
61.87024727431288,
|
||||
62.089902123428836,
|
||||
62.27320880977746,
|
||||
62.42601274014111,
|
||||
62.55327203194878,
|
||||
62.65917552585329,
|
||||
62.74725058582382,
|
||||
62.82045955451526,
|
||||
62.88128472678279,
|
||||
62.931802319077946,
|
||||
62.97374626130382,
|
||||
63.008562806439365
|
||||
],
|
||||
"original_max_position_embeddings": 65536
|
||||
},
|
||||
"rope_theta": 10000.0,
|
||||
"scale_depth": 1.4,
|
||||
"scale_emb": 12,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.46.3",
|
||||
"use_cache": true,
|
||||
"vocab_size": 73448
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
203
configuration_minicpm.py
Normal file
203
configuration_minicpm.py
Normal file
@@ -0,0 +1,203 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2025 The OpenBMB 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.
|
||||
""" MiniCPM model configuration"""
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||||
|
||||
|
||||
class MiniCPMConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
|
||||
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 MiniCPM-7B.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32000):
|
||||
Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`MiniCPMModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||||
The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
|
||||
MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
pad_token_id (`int`, *optional*):
|
||||
Padding token id.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
Beginning of stream token id.
|
||||
eos_token_id (`int`, *optional*, defaults to 2):
|
||||
End of stream token id.
|
||||
pretraining_tp (`int`, *optional*, defaults to 1):
|
||||
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
||||
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
||||
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
||||
issue](https://github.com/pytorch/pytorch/issues/76232).
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
||||
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
||||
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
||||
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
||||
these scaling strategies behave:
|
||||
https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
||||
experimental feature, subject to breaking API changes in future versions.
|
||||
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
||||
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
```python
|
||||
>>> from transformers import MiniCPMModel, MiniCPMConfig
|
||||
|
||||
>>> # Initializing a MiniCPM minicpm-7b style configuration
|
||||
>>> configuration = MiniCPMConfig()
|
||||
|
||||
>>> # Initializing a model from the minicpm-7b style configuration
|
||||
>>> model = MiniCPMModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = 'minicpm'
|
||||
keys_to_ignore_at_inference = ['past_key_values']
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32000,
|
||||
hidden_size=4096,
|
||||
intermediate_size=11008,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
hidden_act='silu',
|
||||
max_position_embeddings=2048,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-6,
|
||||
use_cache=True,
|
||||
pad_token_id=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=True,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
scale_emb=1,
|
||||
dim_model_base=1,
|
||||
scale_depth=1,
|
||||
mup_denominator=32,
|
||||
sparse_config=None,
|
||||
**kwargs):
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.pretraining_tp = pretraining_tp
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
# self._rope_scaling_validation()
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
self.scale_emb = scale_emb
|
||||
self.dim_model_base = dim_model_base
|
||||
self.scale_depth = scale_depth
|
||||
# only used for Eagle Head
|
||||
self.mup_denominator = mup_denominator
|
||||
|
||||
# sparse config
|
||||
self.sparse_config = sparse_config
|
||||
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
import flash_attn
|
||||
self._attn_implementation = 'flash_attention_2'
|
||||
except:
|
||||
pass
|
||||
|
||||
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}")
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"bos_token_id": 1,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
2,
|
||||
73440
|
||||
],
|
||||
"pad_token_id": 2,
|
||||
"temperature": 0.8,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.46.1"
|
||||
}
|
||||
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:24e2a72487b08aea510c3977ae486d6e1e98993b6878de5ab8c6ce7b74a4796f
|
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size 135
|
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3
model-00002-of-00004.safetensors
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3
model-00002-of-00004.safetensors
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@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:37fc0f6fa7786d455faf1c13d7ed30b0df577a2f82bd9263e6e77ff8dbbdfe2b
|
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size 135
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3
model-00003-of-00004.safetensors
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3
model-00003-of-00004.safetensors
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@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c53cc7ab9a4cb2f406bbc8d73618d215d4d885abaa97c4d0410c5417f8c554c
|
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size 135
|
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3
model-00004-of-00004.safetensors
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3
model-00004-of-00004.safetensors
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@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:6d5ae448b9e7a4cbff1e47e6fc3626e67106c729dc93f0e6ec8d0cdbdaeb6df1
|
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size 134
|
||||
299
model.safetensors.index.json
Normal file
299
model.safetensors.index.json
Normal file
@@ -0,0 +1,299 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 16370507776,
|
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2221
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2221
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||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"73445": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"73446": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"73447": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_end|>",
|
||||
"<|im_start|>",
|
||||
"<|tool_call|>",
|
||||
"<|execute_start|>",
|
||||
"<|execute_end|>",
|
||||
"<|fim_prefix|>",
|
||||
"<|fim_middle|>",
|
||||
"<|fim_suffix|>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user