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add olmo and gptj
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Model_Architecture_Discussions/gptj/configuration_gptj.py Normal file
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"""GPT-J model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from transformers import PreTrainedTokenizer, TensorType, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast, PatchingSpec
from transformers.utils import logging
logger = logging.get_logger(__name__)
class GPTJConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
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 GPT-J
[EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. 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 50400):
Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTJModel`].
n_positions (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import GPTJModel, GPTJConfig
>>> # Initializing a GPT-J 6B configuration
>>> configuration = GPTJConfig()
>>> # Initializing a model from the configuration
>>> model = GPTJModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gptj"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50400,
n_positions=2048,
n_embd=4096,
n_layer=28,
n_head=16,
rotary_dim=64,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
tie_word_embeddings=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.rotary_dim = rotary_dim
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
class GPTJOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if not getattr(self._config, "pad_token_id", None):
# TODO: how to do that better?
self._config.pad_token_id = 0
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
else:
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def num_layers(self) -> int:
return self._config.n_layer
@property
def num_attention_heads(self) -> int:
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# We need to order the input in the way they appears in the forward()
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
ordered_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
]
ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
if self.use_past:
mask_dtype = ordered_inputs["attention_mask"].dtype
ordered_inputs["attention_mask"] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
return ordered_inputs
@property
def default_onnx_opset(self) -> int:
return 13

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c5cff513-7872-4207-8877-c1873a58545a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: HF_ENDPOINT=https://hf-mirror.com\n"
]
}
],
"source": [
"%env HF_ENDPOINT=https://hf-mirror.com"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f4c95697-a706-4fd1-bdb5-27f5868d6839",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# 设置 HF_HOME 环境变量 设置下载路径\n",
"os.environ['HF_HOME'] = '/data1/ckw'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "25ca2fbc-b24a-4355-83cb-8e721616e9e7",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58c6aa1afc0a46f98744a945dec66497",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"pytorch_model.bin: 95%|#########4| 22.9G/24.2G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/data1/ckw/micromamba/envs/kewei-ai/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
" return self.fget.__get__(instance, owner)()\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc686f2484084fe9873912c76867507d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/248 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8338c634a3f74ba2a1b398f095802875",
"version_major": 2,
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},
"text/plain": [
"vocab.json: 0.00B [00:00, ?B/s]"
]
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"metadata": {},
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},
{
"data": {
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"model_id": "fad33fcd58a54a5ebe9dd4744ce238aa",
"version_major": 2,
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"text/plain": [
"merges.txt: 0.00B [00:00, ?B/s]"
]
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "73b96b47f93d48f2aac6a54116ad233f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0.00B [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c40d212bd261454583ef354c76f5df60",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"added_tokens.json: 0%| | 0.00/726 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e29e924fccbc435cb6a222718510553d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/357 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\n\\nReferences\\n\\nExternal links'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from transformers import AutoTokenizer\n",
"from modeling_gptj import GPTJForCausalLM\n",
"\n",
"model = GPTJForCausalLM.from_pretrained(\"EleutherAI/gpt-j-6B\")#,force_download=True)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
"\n",
"prompt = '\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.'\n",
"inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
"\n",
"# Generate\n",
"generate_ids = model.generate(inputs.input_ids, max_length=30)\n",
"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "40f99088-fc0e-45c1-b197-758c66cfc0ec",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\n\\nReferences\\n\\nExternal links\\n \\n\\nCategory:Chinese educational websites\\nCategory:Chinese companies established in 2016\\nCategory:Companies based in Shanghai\\nCategory:Educational technology companies of China\\nCategory:Internet properties established in 2016\\nCategory:Shanghai Jiao Tong University\\nCategory:2016 establishments in China'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_ids = model.generate(inputs.input_ids, max_length=300)\n",
"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2944b3cc-1a4d-4577-a3b9-18fd0d3315a4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\n\\nReferences\\n\\nExternal links\\n \\n\\nCategory:Chinese educational websites\\nCategory:Chinese companies established in 2016\\nCategory:Companies based in Shanghai\\nCategory:Educational technology companies of China\\nCategory:Internet properties established in 2016\\nCategory:Shanghai Jiao Tong University\\nCategory:2016 establishments in China'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_ids = model.generate(inputs.input_ids, max_length=500)\n",
"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b07d7e01-672d-4abf-a6b8-720c4dbed783",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "kewei-ai",
"language": "python",
"name": "kewei-ai"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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Model_Architecture_Discussions/olmo/configuration_olmo.py Normal file
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"""OLMo model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class OlmoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OlmoModel`]. It is used to instantiate an OLMo
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 [allenai/OLMo-7B-hf](https://huggingface.co/allenai/OLMo-7B-hf).
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 50304):
Vocabulary size of the OLMo model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`OlmoModel`]
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.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 50279):
End of stream token id.
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/LocalLLaMA/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.
clip_qkv (`float`, *optional*):
If not `None`, elements of query, key and value attention states are clipped so that their
absolute value does not exceed this value.
```python
>>> from transformers import OlmoModel, OlmoConfig
>>> # Initializing a OLMo 7B style configuration
>>> configuration = OlmoConfig()
>>> # Initializing a model from the OLMo 7B style configuration
>>> model = OlmoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "olmo"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50304,
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,
use_cache=True,
pad_token_id=1,
bos_token_id=None,
eos_token_id=50279,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
clip_qkv=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.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.clip_qkv = clip_qkv
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,
)
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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 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}")

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "0364fa99-3cad-4c11-ac41-6523fb98d187",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: HF_ENDPOINT=https://hf-mirror.com\n"
]
}
],
"source": [
"%env HF_ENDPOINT=https://hf-mirror.com"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c654b825-84fd-43df-8412-53b1f9ecb8c7",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# 设置 HF_HOME 环境变量 设置下载路径\n",
"os.environ['HF_HOME'] = '/data1/ckw'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f30fc135-f12f-43bd-96e3-7ab02ef91296",
"metadata": {},
"outputs": [],
"source": [
"# %pip install jieba -q"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "94abdb98-fb74-42c0-805b-03df9fd12311",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37fa141231cf48ec9c6e6e60c8c692cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 16%|#5 | 744M/4.71G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "517e4c72255340fbb08e34a6bddbd0ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"generation_config.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "35428e7696bf4df8aa1ffc047b1c8b39",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/493 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5a318d8c3cbc465382ec4b422909a925",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0.00B [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "768ac1868dd1498a82c969c1abc688dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/65.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.\\nThe company has developed a platform'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from transformers import AutoTokenizer\n",
"from modeling_olmo import OlmoForCausalLM\n",
"\n",
"model = OlmoForCausalLM.from_pretrained(\"allenai/OLMo-1B-hf\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"allenai/OLMo-1B-hf\")\n",
"\n",
"prompt = '\\nDataWhalechina is an organization founded at Shanghai Jiao Tong University that helps learners learn artificial intelligence.'\n",
"inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
"\n",
"# Generate\n",
"generate_ids = model.generate(inputs.input_ids, max_length=30)\n",
"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "61c6597d-3cdd-4a07-aa21-5b27e2cb6914",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\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'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_ids = model.generate(inputs.input_ids, max_length=300)\n",
"tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c503178-b46b-445d-9555-bb529acecb47",
"metadata": {},
"outputs": [],
"source": [
"olmo而是一款不错的模型。生成速度也比较快"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}