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 Settings

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
```

test_v2_medium / README.md

srijithrajamohan

Updates to the line developed by

ce24b8b about 1 year ago

preview code

raw

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	---
	license: mit
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- phi
	- nlp
	- math
	- code
	- chat
	- conversational
	inference:
	parameters:
	temperature: 0
	widget:
	- messages:
	- role: user
	content: How should I explain the Internet?
	library_name: transformers
	---

	# ModernBert Model Card

	[ModernBert Technical Report](https://arxiv.org/pdf/2412.08905)

	## 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.<br><br>`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 \| December 12, 2024 \|
	\| License \| MIT \|