Instructions to use AIFunOver/Phi-3.5-mini-instruct-openvino-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIFunOver/Phi-3.5-mini-instruct-openvino-4bit with Transformers:

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

pipe = pipeline("text-generation", model="AIFunOver/Phi-3.5-mini-instruct-openvino-4bit", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIFunOver/Phi-3.5-mini-instruct-openvino-4bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AIFunOver/Phi-3.5-mini-instruct-openvino-4bit", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIFunOver/Phi-3.5-mini-instruct-openvino-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIFunOver/Phi-3.5-mini-instruct-openvino-4bit

SGLang

How to use AIFunOver/Phi-3.5-mini-instruct-openvino-4bit 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 "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit" \
    --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": "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit",
		"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 "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit" \
        --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": "AIFunOver/Phi-3.5-mini-instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIFunOver/Phi-3.5-mini-instruct-openvino-4bit with Docker Model Runner:
```
docker model run hf.co/AIFunOver/Phi-3.5-mini-instruct-openvino-4bit
```

Phi-3.5-mini-instruct-openvino-4bit

File size: 559 Bytes

7d6f5ec

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  "compression": null,
  "dtype": "int4",
  "input_info": null,
  "optimum_version": "1.23.3",
  "quantization_config": {
    "all_layers": null,
    "bits": 4,
    "dataset": "wikitext2",
    "gptq": null,
    "group_size": 64,
    "ignored_scope": null,
    "num_samples": 20,
    "quant_method": "awq",
    "ratio": 1.0,
    "scale_estimation": true,
    "sensitivity_metric": null,
    "sym": false,
    "tokenizer": null,
    "trust_remote_code": false,
    "weight_format": "int4"
  },
  "save_onnx_model": false,
  "transformers_version": "4.45.2"
}