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Model: cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized
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---
language:
- ko
- en
base_model:
- LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
pipeline_tag: text-generation
tags:
- llm
- exaone
- instruction-tuned
- quantized
- awq
- vllm
- medical
---
# Exaone3.5-7.8B_ReST_V0_Quantized
This model is a fine-tuned and AWQ-quantized version of EXAONE 3.5 7.8B (Instruct), optimized for efficient inference and structured text generation.
## Overview
- Base Model: EXAONE 3.5 7.8B (Instruct)
- Fine-tuning: Supervised fine-tuning on domain-specific data
- Quantization: 4-bit AWQ
- Inference: Optimized for vLLM
- Context Length: up to 32K tokens
## Model Details
- Architecture: ExaoneForCausalLM
- Hidden Size: 4096
- Layers: 32
- Attention Heads: 32
- Max Position Embeddings: 32768
- Quantization: 4-bit AWQ
- Torch dtype: float16
## Intended Use
- Instruction-based text generation
- Structured output generation (JSON)
- LLM-based data pipelines
- RAG systems
- Efficient inference
## Example Usage
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="cococoomo/Exaone3.5-7.8B_ReST_V0_Quantized",
quantization="AWQ",
)
sampling_params = SamplingParams(
temperature=0.2,
top_p=0.8,
max_tokens=1024,
)
outputs = llm.generate(["Your prompt here"], sampling_params)
print(outputs[0].outputs[0].text)
```
## Training
Fine-tuned using supervised learning on domain-specific data.
Dataset is not included due to privacy constraints.
## Limitations
- May produce incorrect outputs
- Sensitive to prompt quality
- Domain bias may exist
## Safety
Not intended for critical decision-making without human validation.
## Evaluation
- BLEU
- ROUGE
## Deployment
Optimized for vLLM and GPU-efficient inference.

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{
"_name_or_path": "/root/.cache/huggingface/hub/models--cococoomo--Exaone3.5-7.8B_ReST_V0_bf16/snapshots/97cf8557d07f238ace29bd8302eb6c71500ec32a",
"activation_function": "silu",
"architectures": [
"ExaoneForCausalLM"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_exaone.ExaoneConfig",
"AutoModelForCausalLM": "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct--modeling_exaone.ExaoneForCausalLM",
"AutoModelForSequenceClassification": "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct--modeling_exaone.ExaoneForSequenceClassification"
},
"bos_token_id": 1,
"embed_dropout": 0.0,
"eos_token_id": 361,
"head_dim": 128,
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"layer_norm_epsilon": 1e-05,
"max_position_embeddings": 32768,
"model_type": "exaone",
"num_attention_heads": 32,
"num_key_value_heads": 8,
"num_layers": 32,
"pad_token_id": 0,
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 1000000.0,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.47.1",
"use_cache": false,
"vocab_size": 102400
}

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# coding=utf-8
# Copyright 2021 The LG AI Research EXAONE Lab. 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.
"""EXAONE model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class ExaoneConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ExaoneModel`]. It is used to
instantiate a EXAONE 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 EXAONE-3.0-7.8B-Instruct [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
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 102400):
Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ExaoneModel`]. Vocabulary size of the model.
Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
[`ExaoneModel`].
max_position_embeddings (`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).
hidden_size (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
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`.
intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
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. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
embed_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon used by 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). Only
relevant if ``config.is_decoder=True``.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
Example:
```python
>>> from transformers import EXAONEModel, ExaoneConfig
>>> # Initializing a EXAONE configuration
>>> configuration = ExaoneConfig()
>>> # Initializing a model from configuration
>>> model = EXAONEModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "exaone"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=102400,
max_position_embeddings=2048,
hidden_size=2048,
num_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
intermediate_size=None,
activation_function="silu",
rope_theta=10000.0,
rope_scaling=None,
embed_dropout=0.0,
attention_dropout=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.num_layers = num_layers
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
if intermediate_size:
self.intermediate_size = intermediate_size
else:
self.intermediate_size = hidden_size * 4
self.activation_function = activation_function
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
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, **kwargs)

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"bos_token_id": 1,
"do_sample": true,
"eos_token_id": 361,
"pad_token_id": 0,
"transformers_version": "4.47.1"
}

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