Instructions to use KitsuVp/NeoLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KitsuVp/NeoLLM with Transformers:

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

pipe = pipeline("text-generation", model="KitsuVp/NeoLLM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KitsuVp/NeoLLM", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KitsuVp/NeoLLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KitsuVp/NeoLLM

SGLang

How to use KitsuVp/NeoLLM 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 "KitsuVp/NeoLLM" \
    --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": "KitsuVp/NeoLLM",
		"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 "KitsuVp/NeoLLM" \
        --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": "KitsuVp/NeoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KitsuVp/NeoLLM with Docker Model Runner:
```
docker model run hf.co/KitsuVp/NeoLLM
```

KitsuVp commited on Sep 16, 2025

Commit

04da2a7

verified ·

1 Parent(s): d081347

Update modeling_neollm.py

Browse files

Files changed (1) hide show

modeling_neollm.py +7 -7

modeling_neollm.py CHANGED Viewed

@@ -30,6 +30,7 @@ from transformers.utils.import_utils import (
 )
 from configuration_neollm import NeoLLMConfig
 if is_causal_conv1d_available():
     from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
@@ -1016,6 +1017,12 @@ class NeoLLMForCausalLM(NeoLLMPreTrainedModel, GenerationMixin):
             attentions=outputs.attentions,
         )
 __all__ = [
     "NeoLLMForCausalLM",
@@ -1025,10 +1032,3 @@ __all__ = [
     "FANLayer",
 ]
-# ==================== AUTOMODEL REGISTRATION ====================
-from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
-# Register the configuration and model for AutoClass support
-AutoConfig.register("neollm", NeoLLMConfig)
-AutoModel.register(NeoLLMConfig, NeoLLMModel)
-AutoModelForCausalLM.register(NeoLLMConfig, NeoLLMForCausalLM)

 )
 from configuration_neollm import NeoLLMConfig
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
 if is_causal_conv1d_available():
     from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
             attentions=outputs.attentions,
         )
+# ==================== AUTOMODEL REGISTRATION ====================
+# Register the configuration and model for AutoClass support
+AutoConfig.register("neollm", NeoLLMConfig)
+AutoModel.register(NeoLLMConfig, NeoLLMModel)
+AutoModelForCausalLM.register(NeoLLMConfig, NeoLLMForCausalLM)
 __all__ = [
     "NeoLLMForCausalLM",
     "FANLayer",
 ]