mirror of
https://github.com/datawhalechina/llms-from-scratch-cn.git
synced 2026-06-06 00:04:42 +00:00
add olmo
This commit is contained in:
kewei
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"""OLMo 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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class OlmoConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`OlmoModel`]. It is used to instantiate an OLMo
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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 [allenai/OLMo-7B-hf](https://huggingface.co/allenai/OLMo-7B-hf).
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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 50304):
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Vocabulary size of the OLMo model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`OlmoModel`]
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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.
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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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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*, defaults to 1):
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Padding token id.
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bos_token_id (`int`, *optional*):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 50279):
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End of stream token id.
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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/LocalLLaMA/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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clip_qkv (`float`, *optional*):
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If not `None`, elements of query, key and value attention states are clipped so that their
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absolute value does not exceed this value.
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```python
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>>> from transformers import OlmoModel, OlmoConfig
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>>> # Initializing a OLMo 7B style configuration
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>>> configuration = OlmoConfig()
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>>> # Initializing a model from the OLMo 7B style configuration
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>>> model = OlmoModel(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 = "olmo"
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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=50304,
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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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use_cache=True,
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pad_token_id=1,
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bos_token_id=None,
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eos_token_id=50279,
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tie_word_embeddings=False,
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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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clip_qkv=None,
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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.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.clip_qkv = clip_qkv
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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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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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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 two fields, `type` and `factor`, " 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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Load Diff
@@ -0,0 +1,200 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0364fa99-3cad-4c11-ac41-6523fb98d187",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"env: HF_ENDPOINT=https://hf-mirror.com\n"
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]
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}
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],
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"source": [
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"%env HF_ENDPOINT=https://hf-mirror.com"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c654b825-84fd-43df-8412-53b1f9ecb8c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# 设置 HF_HOME 环境变量 设置下载路径\n",
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"os.environ['HF_HOME'] = '/data1/ckw'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f30fc135-f12f-43bd-96e3-7ab02ef91296",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install jieba -q"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "94abdb98-fb74-42c0-805b-03df9fd12311",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "37fa141231cf48ec9c6e6e60c8c692cb",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"model.safetensors: 16%|#5 | 744M/4.71G [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "517e4c72255340fbb08e34a6bddbd0ce",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"generation_config.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "35428e7696bf4df8aa1ffc047b1c8b39",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"tokenizer_config.json: 0%| | 0.00/493 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5a318d8c3cbc465382ec4b422909a925",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"tokenizer.json: 0.00B [00:00, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "768ac1868dd1498a82c969c1abc688dc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"special_tokens_map.json: 0%| | 0.00/65.0 [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\nThe company has developed a platform'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from transformers import AutoTokenizer\n",
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"from modeling_olmo import OlmoForCausalLM\n",
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"\n",
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"model = OlmoForCausalLM.from_pretrained(\"allenai/OLMo-1B-hf\")\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"allenai/OLMo-1B-hf\")\n",
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"\n",
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"prompt = '\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.'\n",
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"inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
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"\n",
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"# Generate\n",
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"generate_ids = model.generate(inputs.input_ids, max_length=30)\n",
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"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "61c6597d-3cdd-4a07-aa21-5b27e2cb6914",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\nThe company has developed a platform that allows users to learn AI by watching videos and answering questions.\\nThe platform is designed to be easy to use and has a variety of features that make it a great choice for learners.\\nDataWhalechina is a platform that helps learners learn artificial intelligence.\\nThe platform is designed to be easy to use and has a variety of features that make it a great choice for learners.\\nThe platform is also designed to be flexible, so that it can be used by different types of learners.\\nDataWhalechina is a platform that helps learners learn artificial intelligence.\\nThe platform is designed to be easy to use and has a variety of features that make it a great choice for learners.\\nThe platform is also designed to be flexible, so that it can be used by different types of learners.\\nDataWhalechina is a platform that helps learners learn artificial intelligence.\\nThe platform is designed to be easy to use and has a variety of features that make it a great choice for learners.\\nThe platform is also designed to be flexible, so that it can be used by different types of learners.\\nDataWhalechina is a platform that helps learners learn artificial intelligence.\\nThe platform is designed to be easy to use and has a variety of features that make it a great choice for learners.\\nThe'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"generate_ids = model.generate(inputs.input_ids, max_length=300)\n",
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"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6c503178-b46b-445d-9555-bb529acecb47",
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"metadata": {},
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"outputs": [],
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"source": [
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"olmo而是一款不错的模型。生成速度也比较快"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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Reference in New Issue
Block a user