初始化项目，由ModelHub XC社区提供模型
Model: mlx-community/MiniCPM3-4B-bfloat16 Source: Original Platform
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39
README.md
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README.md
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---
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license: apache-2.0
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language:
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- zh
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- mlx
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base_model: Goekdeniz-Guelmez/OpenBNB-MiniCPM3-4b
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---
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# mlx-community/MiniCPM3-4B-bfloat16
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The Model [mlx-community/MiniCPM3-4B-bfloat16](https://huggingface.co/mlx-community/MiniCPM3-4B-bfloat16) was
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converted to MLX format from [Goekdeniz-Guelmez/OpenBNB-MiniCPM3-4b](https://huggingface.co/Goekdeniz-Guelmez/OpenBNB-MiniCPM3-4b)
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using mlx-lm version **0.22.0**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("mlx-community/MiniCPM3-4B-bfloat16")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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added_tokens.json
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added_tokens.json
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{
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}
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78
config.json
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config.json
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{
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"architectures": [
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"MiniCPM3ForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_minicpm.MiniCPM3Config",
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"AutoModel": "modeling_minicpm.MiniCPM3Model",
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"AutoModelForCausalLM": "modeling_minicpm.MiniCPM3ForCausalLM",
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"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPM3ForCausalLM",
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"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPM3ForSequenceClassification"
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},
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"bos_token_id": 1,
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"dim_model_base": 256,
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"eos_token_id": [
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2,
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73440
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"hidden_act": "silu",
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"intermediate_size": 6400,
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"kv_lora_rank": 256,
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"max_position_embeddings": 32768,
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"model_type": "minicpm3",
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"num_attention_heads": 40,
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"num_hidden_layers": 62,
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"num_key_value_heads": 40,
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"q_lora_rank": 768,
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"qk_nope_head_dim": 64,
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"qk_rope_head_dim": 32,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"type": "longrope",
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13.062047352306353,
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14.85530648787323,
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16.87581087164081,
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16.940876586213285
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],
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||||
"short_factor": [
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||||
1.0591234137867171,
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1.1241891283591912,
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1.2596935748670968,
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1.5380380402321725,
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2.093982484148734,
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3.1446935121267696,
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7.524541999994549,
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10.475458000005451,
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14.85530648787323,
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15.906017515851266,
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"original_max_position_embeddings": 32768
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||||
},
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||||
"scale_depth": 1.4,
|
||||
"scale_emb": 12,
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||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.41.0",
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||||
"use_cache": true,
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"vocab_size": 73448
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}
|
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1
configuration.json
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1
configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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195
configuration_minicpm.py
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configuration_minicpm.py
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# coding=utf-8
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# Copyright 2024 The OpenBMB team and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
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# and OPT implementations in this library. It has been modified from its
|
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# original forms to accommodate minor architectural differences compared
|
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
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#
|
||||
# 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"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class MiniCPM3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the MiniCPM-7B.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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|
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
|
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Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MiniCPMModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations.
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||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
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||||
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
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||||
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.
|
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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
|
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
|
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
|
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
|
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End of stream token id.
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||||
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).
|
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
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Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
|
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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.
|
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attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
```python
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>>> from transformers import MiniCPMModel, MiniCPMConfig
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|
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>>> # Initializing a MiniCPM minicpm-7b style configuration
|
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>>> configuration = MiniCPMConfig()
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>>> # Initializing a model from the minicpm-7b style configuration
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>>> model = MiniCPMModel(configuration)
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>>> # Accessing the model configuration
|
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>>> configuration = model.config
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```"""
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|
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model_type = "minicpm3"
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keys_to_ignore_at_inference = ["past_key_values"]
|
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|
||||
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,
|
||||
qk_nope_head_dim=64,
|
||||
qk_rope_head_dim=32,
|
||||
q_lora_rank=768,
|
||||
kv_lora_rank=256,
|
||||
v_head_dim=None,
|
||||
head_dim=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,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.qk_nope_head_dim = qk_nope_head_dim
|
||||
self.qk_rope_head_dim = qk_rope_head_dim
|
||||
self.q_lora_rank = q_lora_rank
|
||||
self.kv_lora_rank = kv_lora_rank
|
||||
|
||||
if v_head_dim is None:
|
||||
v_head_dim = qk_nope_head_dim
|
||||
self.v_head_dim = v_head_dim
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
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||||
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.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
|
||||
self.head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
|
||||
|
||||
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
|
||||
3
model-00001-of-00002.safetensors
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3
model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ff12f8af3651ae27a26bba11afe3e5365040a013f8fc7224a9a564c11bb8f52
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size 5357327357
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3
model-00002-of-00002.safetensors
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3
model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5bf08479c08dde25ab2725415612001d323bc965196a2500275cb6bd768cff4c
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||||
size 3166566305
|
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754
model.safetensors.index.json
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754
model.safetensors.index.json
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{
|
||||
"metadata": {
|
||||
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1572
modeling_minicpm.py
Normal file
1572
modeling_minicpm.py
Normal file
File diff suppressed because it is too large
Load Diff
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:adf7208af154a5ca065d2eda4e5419e02aac58c2c00627874748b75ec6769094
|
||||
size 6701371
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530
|
||||
size 1181204
|
||||
118
tokenizer_config.json
Normal file
118
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user