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Model: KORMo-Team/KORMo-10B-base
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---
library_name: transformers
license: apache-2.0
---
<!-- <p align="center">
<img src="https://github.com/MLP-Lab/KORMo-tutorial/blob/main/tutorial/attachment/kormo_logo.png?raw=true" style="width: 100%; max-width: 1100px;">
</p> -->
<p align="center">
<img src="https://github.com/MLP-Lab/KORMo-tutorial/blob/main/tutorial/attachment/kormo_logo.svg?raw=true" style="width: 40%; max-width: 1100px;">
</p>
## 🚀 Update News
- **2025-10-13**: Official release of KORMo-10B-base (Be aware that it's not an SFT model!!).
---
## 💡 About KORMo
**KORMo-10B** is a **10.8B parameter fully open LLM** capable of handling both **Korean and English**.
The model, training code, and training data are all **fully open**, allowing anyone to reproduce and extend them.
- **Model Size**: 10.8B parameters
- **Languages**: Korean / English
- **Training Data**: Synthetic data + public datasets (approximately 3T tokens)
- **License**: Apache 2.0
```md
The First Fully Open-Source LLM from a Non-English Region
KORMo was created with a public-interest mission: to make world-class language models accessible to everyone.
Our goal is to empower anyone to build and advance their own large language models at a global standard.
Key Features:
1. A 10B-parameter Korean–English reasoning model trained entirely from scratch.
2. 100% open resources — including all training data, code, intermediate checkpoints, and tutorials — allowing anyone to reproduce and extend a near-SOTA model on their own.
3. 3 trillion tokens of training data released publicly, featuring never-before-shared, high-quality full-cycle Korean datasets (for pretraining, post-training, general, reasoning, and reinforcement learning).
4. A collaborative effort by eight master’s students at the KAIST Graduate School of Culture Technology (MLP Lab), documented in a 45-page research paper.
If you’ve ever used a Korean language model that performs well on benchmarks but feels strange in real use, or if fine-tuning only made it worse, you’re not alone.
KORMo solves these problems head-on.
By releasing every intermediate model and post-training dataset, we give users the freedom to build on the base model with their own data, customizing and fine-tuning it in any direction they want.
👉 "If you want a great Korean language model, now you can build it yourself. It even works with free Colab GPUs!" 🤗
```
---
## 🔗 Links
- 📖 **Technical Report**: [👉 Arxive](https://arxiv.org/pdf/2510.09426)
- 🤗 **Hugging Face**: [👉 Model Download](https://huggingface.co/KORMo-Team)
- 💻 **GitHub Repository**: [👉 Training and Inference Code](https://github.com/MLP-Lab/KORMo-tutorial)
- 🔉 **Tutorial**: [👉 Instruction Tuning over google colab](https://colab.research.google.com/github/MLP-Lab/KORMo-tutorial/blob/main/tutorial/02.sft_qlora.ipynb) [👉 Youtube Tutorial](https://www.youtube.com/@MLPLab)
---
## 📈 Benchmark Performance
### 📊 Quantitative Evaluation
| Benchmark | **KORMo-10B** | smolLM3-3B | olmo2-7B | olmo2-13B | kanana1.5-8B | qwen3-8B | llama3.1-8B | gemma3-4B | gemma3-12B |
|:-----------|---------------:|-----------:|---------:|---------:|------------:|--------:|-----------:|---------:|----------:|
| **🇺🇸 English Benchmarks** |||||||||||
| arc_challenge | 58.96 | 55.55 | 59.13 | 61.01 | 56.48 | 63.82 | 54.61 | 53.58 | 63.82 |
| arc_easy | 85.48 | 83.21 | 85.06 | 86.57 | 82.74 | 87.50 | 84.01 | 82.83 | 87.37 |
| boolq | 83.46 | 82.17 | 84.50 | 86.48 | 84.53 | 87.71 | 81.87 | 80.70 | 86.61 |
| copa | 93.00 | 91.00 | 92.00 | 93.00 | 88.00 | 92.00 | 93.00 | 89.00 | 95.00 |
| gpqa_main | 30.13 | 26.79 | 26.34 | 29.24 | 29.24 | 30.13 | 23.44 | 30.13 | 35.71 |
| hellaswag | 60.25 | 56.78 | 61.52 | 65.02 | 59.93 | 59.54 | 60.96 | 57.56 | 63.67 |
| mmlu | 67.96 | 61.37 | 62.81 | 66.85 | 63.73 | 76.95 | 65.03 | 59.60 | 73.58 |
| mmlu_global | 63.44 | 57.52 | 59.88 | 63.99 | 60.21 | 75.05 | 61.30 | 57.23 | 70.23 |
| mmlu_pro | 40.18 | 34.94 | 27.29 | 32.50 | 34.93 | 56.58 | 36.23 | 27.79 | 37.07 |
| mmlu_redux | 69.00 | 62.95 | 63.53 | 68.37 | 65.88 | 78.19 | 65.86 | 60.86 | 75.25 |
| openbookqa | 39.00 | 36.40 | 39.00 | 39.60 | 36.80 | 39.20 | 39.00 | 37.00 | 40.20 |
| piqa | 81.12 | 78.45 | 80.79 | 82.64 | 80.30 | 79.05 | 80.90 | 79.49 | 82.59 |
| social_iqa | 52.81 | 50.72 | 55.89 | 57.57 | 57.01 | 56.96 | 53.12 | 51.84 | 56.45 |
| **English Avg.** | **63.45** | 59.83 | 61.36 | 64.06 | 61.52 | 67.90 | 61.49 | 59.05 | 66.73 |
| **🇰🇷 Korean Benchmarks** |||||||||||
| click | 55.29 | 46.97 | 37.79 | 41.80 | 62.76 | 60.70 | 49.22 | 49.62 | 62.21 |
| csatqa | 38.00 | 26.67 | 19.33 | 24.67 | 44.67 | 52.00 | 28.67 | 28.67 | 31.33 |
| haerae | 68.29 | 55.82 | 31.62 | 37.58 | 80.75 | 67.19 | 53.25 | 60.68 | 74.34 |
| k2_eval | 84.89 | 75.23 | 49.54 | 63.43 | 84.72 | 84.72 | 76.62 | 76.39 | 85.42 |
| kobest | 75.05 | 69.13 | 57.27 | 59.02 | 81.93 | 80.05 | 70.55 | 69.33 | 77.70 |
| kobalt | 22.86 | 15.86 | 11.43 | 13.14 | 26.29 | 26.57 | 17.43 | 15.57 | 23.86 |
| kmmlu | 46.48 | 38.52 | 33.05 | 31.24 | 48.86 | 56.93 | 40.75 | 39.84 | 51.60 |
| mmlu_global (ko) | 55.16 | 44.15 | 34.00 | 36.95 | 52.65 | 61.95 | 46.34 | 46.33 | 59.68 |
| kr_clinical_qa | 77.32 | 53.97 | 48.33 | 46.22 | 65.84 | 80.00 | 63.54 | 60.00 | 77.22 |
| **Korean Avg.** | **58.15** | 47.37 | 35.82 | 39.34 | 60.94 | 63.35 | 49.60 | 49.60 | 60.37 |
### 📝 Qualitative Evaluation (LLM-as-a-Judge)
| Benchmark | KORMo-10B | smolLM3-3B | olmo2-7B | olmo2-13B | kanana1.5-8B | qwen3-8B | llama3.1-8B | exaone3.5-8B | gemma3-12B |
|:----------|---------:|----------:|---------:|---------:|------------:|--------:|------------:|-------------:|-----------:|
| MT-Bench (EN) | 8.32 | 7.15 | 7.32 | 7.64 | 8.45 | 8.70 | 6.32 | 8.15 | 8.70 |
| KO-MT-Bench (KO) | 8.54 | - | - | - | 8.02 | 8.16 | 4.27 | 8.13 | 8.51 |
| LogicKor (KO) | 8.96 | - | - | - | 8.94 | 8.63 | 6.45 | 9.20 | 8.46 |
| **Average** | **8.61** | - | - | - | **8.47** | **8.50** | **5.68** | **8.49** | **8.56** |
---
## 📦 Installation
```bash
git clone https://github.com/MLP-Lab/KORMo-tutorial.git
cd KORMo-tutorial
bash setup/create_uv_venv.sh
source .venv_kormo/bin/activate
```
---
## 🚀 Inference Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "KORMo-Team/KORMo-10B-sft"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "user", "content": "What happens inside a black hole?"}
]
chat_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
output_ids = model.generate(
**inputs,
max_new_tokens=1024,
)
response = tokenizer.decode(output_ids[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print("Assistant:", response)
```
## 🧠 Enabling Thinking Mode
If you want to enable the **thinking** mode, simply set `enable_thinking=True`:
```python
chat_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True
)
```
---
## Contact
- KyungTae Lim, Professor at KAIST. `ktlim@kaist.ac.kr`
## Acknowledgments
- This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2025-02653113, High-Performance Research AI Computing Infrastructure Support at the 2 PFLOPS Scale)
## Citation
```text
@misc{KORMo,
author = {Minjun Kim, Hyeonseok Lim, Hangyeol Yoo, Inho Won, Seungwoo Song, Minkyung Cho, Junghun Yuk, Changsu Choi, Dongjae Shin, Huije Lee, Hoyun Song, Alice Oh and KyungTae Lim},
title = {KORMo: Korean Open Reasoning Model for Everyone},
year = {2025},
publisher = {GitHub},
journal = {Technical Report},
paperLink = {\url{https://arxiv.org/abs/2510.09426}},
},
}
```

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from transformers import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class KORMoConfig(PretrainedConfig):
model_type = "kormo"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
def __init__(
self,
vocab_size=112576,
hidden_size=6144,
intermediate_size=21504,
num_hidden_layers=48,
num_attention_heads=40,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=131072,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=500000.0,
attention_bias=False,
attention_dropout=0.0,
rope_scaling=None,
mlp_bias=False,
head_dim=128,
**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
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.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
self.mask_type = None
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
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,
)

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from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import nn
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.generation import GenerationMixin
from transformers.integrations import use_kernel_forward_from_hub
from transformers.masking_utils import create_causal_mask
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import can_return_tuple, logging
from ._configuration_kormo import KORMoConfig
logger = logging.get_logger(__name__)
@use_kernel_forward_from_hub("RMSNorm")
class RMSNorm(nn.Module):
"""
KORMoRMSNorm is equivalent to T5LayerNorm
"""
def __init__(self, hidden_size: int, eps: float = 1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight * hidden_states).to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed.to(q.dtype), k_embed.to(k.dtype)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: KORMoConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(hidden_shape).transpose(1, 2)
key_states = key_states.view(hidden_shape).transpose(1, 2)
value_states = value_states.view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
@use_kernel_forward_from_hub("MLP")
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
output = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return output
class DecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: KORMoConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Attention(config=config, layer_idx=layer_idx)
self.mlp = MLP(config)
self.pre_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pre_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.pre_attention_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states
# MLP layer
residual = hidden_states
hidden_states = self.pre_mlp_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class RotaryEmbedding(nn.Module):
def __init__(self, config: KORMoConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos, sin
class KORMoPreTrainedModel(PreTrainedModel):
config_class = KORMoConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_3 = True
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, RMSNorm):
module.weight.data.fill_(1.0)
class KORMoModel(KORMoPreTrainedModel):
def __init__(self, config: KORMoConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = RotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if not isinstance(past_key_values, (type(None), Cache)):
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if self.config._attn_implementation == "flash_attention_3_doc":
### TODO: 수정필요
causal_mask = attention_mask
else:
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids)
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class KORMoForCausalLM(KORMoPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.model = KORMoModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: int = 0,
**kwargs,
) -> CausalLMOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)

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{
"architectures": [
"KORMoForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "_configuration_kormo.KORMoConfig",
"AutoModel": "_modeling_kormo.KORMoModel",
"AutoModelForCausalLM": "_modeling_kormo.KORMoForCausalLM"
},
"bos_token_id": 125030,
"dtype": "bfloat16",
"eos_token_id": 125031,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 16384,
"mask_type": null,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "kormo",
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"num_hidden_layers": 40,
"num_key_value_heads": 8,
"pretrain_tp": 1,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"tie_word_embeddins": false,
"transformers_version": "4.57.0",
"use_cache": true,
"vocab_size": 125184
}

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