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Parent(s):
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8cfb6dc951cf1918d43cf41b29bc9ce2073bd949b81207f5f7328d93e4f36a84
Browse files- README.md +28 -0
- config.json +32 -0
- configuration_minicpm.py +202 -0
- model-00002-of-00002.safetensors +3 -0
README.md
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---
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language:
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- en
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- zh
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library_name: mlx
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tags:
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- MiniCPM
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- ModelBest
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- THUNLP
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- mlx
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- mlx
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---
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# mlx-community/MiniCPM-2B-dpo-bf16
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This model was converted to MLX format from [`Isaak-Carter/MiniCPM-2B-dpo-bf16-safetensors`]() using mlx-lm version **0.12.1**.
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Refer to the [original model card](https://huggingface.co/Isaak-Carter/MiniCPM-2B-dpo-bf16-safetensors) for more details on the model.
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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/MiniCPM-2B-dpo-bf16")
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response = generate(model, tokenizer, prompt="hello", verbose=True)
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```
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config.json
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{
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"architectures": [
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"MiniCPMForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_minicpm.MiniCPMConfig",
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"AutoModel": "modeling_minicpm.MiniCPMModel",
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"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
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"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
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"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
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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": 2,
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"hidden_act": "silu",
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"hidden_size": 2304,
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"initializer_range": 0.1,
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"intermediate_size": 5760,
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"max_position_embeddings": 4096,
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"model_type": "minicpm",
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"num_attention_heads": 36,
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"num_hidden_layers": 40,
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"num_key_value_heads": 36,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"scale_depth": 1.4,
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"scale_emb": 12,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.0",
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"use_cache": true,
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"vocab_size": 122753
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}
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configuration_minicpm.py
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# coding=utf-8
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# Copyright 2022 EleutherAI 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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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" 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 MiniCPMConfig(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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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):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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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
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
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MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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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):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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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
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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
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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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):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import MiniCPMModel, MiniCPMConfig
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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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model_type = "minicpm"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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scale_emb=1,
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dim_model_base=1,
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scale_depth=1,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.scale_emb = scale_emb
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self.dim_model_base = dim_model_base
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self.scale_depth = scale_depth
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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try:
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import flash_attn
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self._attn_implementation = "flash_attention_2"
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except:
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pass
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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model-00002-of-00002.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:93d8b4b5449e39b249ab0f00c78f3cb69d2b47da8954a179d595908fc3d08be7
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size 655903609
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