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", device_map="auto")

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
```

srijithrajamohan commited on Mar 13, 2025

Commit

56f2ae6

1 Parent(s): 53aeb62

Testing model card updates

Browse files

Files changed (1) hide show

README.md +40 -3

README.md CHANGED Viewed

@@ -1,7 +1,44 @@
 ---
 license: mit
 ---
-language:
-  - en
-  - de
 ---

 ---
 license: mit
 ---
+- 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
 ---
+# Phi-4 Model Card
+[Phi-4 Technical Report](https://arxiv.org/pdf/2412.08905)
+## Model Summary
+|                         |                                                                               |
+|-------------------------|-------------------------------------------------------------------------------|
+| **Developers**          | Microsoft Research                                                            |
+| **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                                                                         |