Instructions to use sjster/test_v2_medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sjster/test_v2_medium with Transformers:

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

pipe = pipeline("text-generation", model="sjster/test_v2_medium")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("sjster/test_v2_medium")
model = AutoModelForMaskedLM.from_pretrained("sjster/test_v2_medium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sjster/test_v2_medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sjster/test_v2_medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sjster/test_v2_medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sjster/test_v2_medium

SGLang

How to use sjster/test_v2_medium 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 "sjster/test_v2_medium" \
    --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": "sjster/test_v2_medium",
		"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 "sjster/test_v2_medium" \
        --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": "sjster/test_v2_medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sjster/test_v2_medium with Docker Model Runner:
```
docker model run hf.co/sjster/test_v2_medium
```

ModernBert Model Card

ModernBert Technical Report

Model Summary


Developed by	Micro
Description	`phi-4` is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. `phi-4` underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures
Architecture	14B parameters, dense decoder-only Transformer model
Inputs	Text, best suited for prompts in the chat format
Context length	16K tokens
GPUs	1920 H100-80G
Training time	21 days
Training data	9.8T tokens
Outputs	Generated text in response to input
Dates	October 2024 – November 2024
Status	Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data
Release date	March 17, 2025
License	MIT

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Safetensors

Model size

0.1B params

Tensor type

F32

Paper for sjster/test_v2_medium

Phi-4 Technical Report

Paper • 2412.08905 • Published Dec 12, 2024 • 123