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Model: OpenBuddy/openbuddy-falcon-7b-v6-bf16
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2026-06-01 07:57:12 +08:00
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---
license: apache-2.0
---

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{
"_name_or_path": "/data/output/final/",
"alibi": false,
"apply_residual_connection_post_layernorm": false,
"architectures": [
"RWForCausalLM"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_RW.RWConfig",
"AutoModel": "modelling_RW.RWModel",
"AutoModelForCausalLM": "modelling_RW.RWForCausalLM",
"AutoModelForQuestionAnswering": "modelling_RW.RWForQuestionAnswering",
"AutoModelForSequenceClassification": "modelling_RW.RWForSequenceClassification",
"AutoModelForTokenClassification": "modelling_RW.RWForTokenClassification"
},
"bias": false,
"bos_token_id": 11,
"eos_token_id": 11,
"hidden_dropout": 0.0,
"hidden_size": 4544,
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"model_type": "RefinedWebModel",
"multi_query": true,
"n_head": 71,
"n_layer": 32,
"parallel_attn": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.30.2",
"use_cache": true,
"vocab_size": 70144
}

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{"framework":"Pytorch","task":"text-generation"}

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# coding=utf-8
# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Bloom configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class RWConfig(PretrainedConfig):
model_type = "RefinedWebModel"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__(
self,
vocab_size=250880,
hidden_size=64,
n_layer=2,
n_head=8,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
apply_residual_connection_post_layernorm=False,
hidden_dropout=0.0,
attention_dropout=0.0,
multi_query=False,
alibi=False,
bias=False,
parallel_attn=False,
**kwargs,
):
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
n_embed = kwargs.pop("n_embed", None)
self.hidden_size = hidden_size if n_embed is None else n_embed
self.n_layer = n_layer
self.n_head = n_head
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.multi_query = multi_query
self.alibi = alibi
self.bias = bias
self.parallel_attn = parallel_attn
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):
return self.hidden_size // self.n_head
@property
def rotary(self):
return not self.alibi

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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"transformers_version": "4.30.2"
}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
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">>SUMMARY<<",
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],
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}

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tokenizer.json Normal file
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7
tokenizer_config.json Normal file
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{
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"clean_up_tokenization_spaces": true,
"eos_token": "<|endoftext|>",
"model_max_length": 2048,
"tokenizer_class": "PreTrainedTokenizerFast"
}