Instructions to use KORMo-Team/KORMo-10B-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KORMo-Team/KORMo-10B-sft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="KORMo-Team/KORMo-10B-sft", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KORMo-Team/KORMo-10B-sft", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KORMo-Team/KORMo-10B-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KORMo-Team/KORMo-10B-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KORMo-Team/KORMo-10B-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KORMo-Team/KORMo-10B-sft

SGLang

How to use KORMo-Team/KORMo-10B-sft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "KORMo-Team/KORMo-10B-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KORMo-Team/KORMo-10B-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "KORMo-Team/KORMo-10B-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KORMo-Team/KORMo-10B-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KORMo-Team/KORMo-10B-sft with Docker Model Runner:
```
docker model run hf.co/KORMo-Team/KORMo-10B-sft
```

mjkmain commited on Oct 12, 2025

Commit

cfa99fc

verified ·

1 Parent(s): ec64b46

Update _modeling_kormo.py

Browse files

Files changed (1) hide show

_modeling_kormo.py +1 -7

_modeling_kormo.py CHANGED Viewed

@@ -1,7 +1,6 @@
 from typing import Callable, List, Optional, Tuple, Union
 import torch
-import torch.utils.checkpoint ### ADD
 from torch import nn
 from transformers.activations import ACT2FN
@@ -17,13 +16,10 @@ from transformers.modeling_outputs import (
 )
 from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-# from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.processing_utils import Unpack
-from transformers.utils import auto_docstring, can_return_tuple, logging
 from ._configuration_kormo import KORMoConfig
-# from ._flash_attn3_doc import flash_attention_3_doc_forward
-# ALL_ATTENTION_FUNCTIONS._global_mapping.update({'flash_attention_3_doc': flash_attention_3_doc_forward})
 logger = logging.get_logger(__name__)
@@ -421,8 +417,6 @@ class KORMoModel(KORMoPreTrainedModel):
         )
-# class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
 class KORMoForCausalLM(KORMoPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
     _tp_plan = {"lm_head": "colwise_rep"}

 from typing import Callable, List, Optional, Tuple, Union
 import torch
 from torch import nn
 from transformers.activations import ACT2FN
 )
 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__)
         )
 class KORMoForCausalLM(KORMoPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
     _tp_plan = {"lm_head": "colwise_rep"}