Instructions to use kotlarmilos/dotnet-runtime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kotlarmilos/dotnet-runtime with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-mini-instruct")
model = PeftModel.from_pretrained(base_model, "kotlarmilos/dotnet-runtime")

Transformers

How to use kotlarmilos/dotnet-runtime with Transformers:

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

pipe = pipeline("text-generation", model="kotlarmilos/dotnet-runtime")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kotlarmilos/dotnet-runtime", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kotlarmilos/dotnet-runtime with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kotlarmilos/dotnet-runtime

SGLang

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

Docker Model Runner
How to use kotlarmilos/dotnet-runtime with Docker Model Runner:
```
docker model run hf.co/kotlarmilos/dotnet-runtime
```

kotlarmilos commited on Jul 30, 2025

Commit

d5031d4

verified ·

1 Parent(s): e3d78b4

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -150,3 +150,7 @@ The model was fine-tuned on approximately 10,000 high-quality instruction-respon
 - **Training Method**: LoRA (Low-Rank Adaptation)
 - **Quantization**: 4-bit NF4 with BitsAndBytes
 - **Training regime**: Mixed precision training with appropriate optimization

 - **Training Method**: LoRA (Low-Rank Adaptation)
 - **Quantization**: 4-bit NF4 with BitsAndBytes
 - **Training regime**: Mixed precision training with appropriate optimization
+## Usage Examples
+If you use this model, please refer to https://github.com/kotlarmilos/phi4-finetuned