Instructions to use MILVLG/Imp-v1.5-4B-Phi3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MILVLG/Imp-v1.5-4B-Phi3 with Transformers:

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

pipe = pipeline("text-generation", model="MILVLG/Imp-v1.5-4B-Phi3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MILVLG/Imp-v1.5-4B-Phi3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MILVLG/Imp-v1.5-4B-Phi3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MILVLG/Imp-v1.5-4B-Phi3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MILVLG/Imp-v1.5-4B-Phi3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MILVLG/Imp-v1.5-4B-Phi3

SGLang

How to use MILVLG/Imp-v1.5-4B-Phi3 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 "MILVLG/Imp-v1.5-4B-Phi3" \
    --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": "MILVLG/Imp-v1.5-4B-Phi3",
		"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 "MILVLG/Imp-v1.5-4B-Phi3" \
        --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": "MILVLG/Imp-v1.5-4B-Phi3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MILVLG/Imp-v1.5-4B-Phi3 with Docker Model Runner:
```
docker model run hf.co/MILVLG/Imp-v1.5-4B-Phi3
```

Imp-v1.5-4B-Phi3 / model-00009-of-00009.safetensors

Oyoy1235

Imp-v1.5-4b update

e37c108 over 2 years ago

Download

History Blame Contribute Delete

599 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: c86299bef33ad4dce7b69735bdb1016afe3f2b3a1fdd035b1a541fb5f059116e
Size of remote file:: 599 MB
SHA256:: d38fcded882b7e25c4fa224bf1fea6a675dd8abf3db234645a1a81d471654930

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.