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

Libraries

How to use microsoft/Phi-3.5-mini-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3.5-mini-instruct", 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("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Phi-3.5-mini-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/Phi-3.5-mini-instruct

SGLang

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

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

Evaluation of Phi-3.5 on long-context BABILong bench

#12

by yurakuratov - opened Aug 23, 2024

Discussion

yurakuratov

Aug 23, 2024

Hi! I want to share some promising results of the new Phi-3.5 models on the BABILong benchmark, which evaluates models' long-context reasoning over multiple distributed facts.

The new Phi-3.5 models show much better results on the BABILong benchmark compared to previous versions and larger competitor models. The Phi-3.5-mini-instruct model notably improves on context lengths up to 32K, outperforming the Phi-3-mini-128k-instruct. The Phi-3.5-MoE-instruct achieves similar performance to the Phi-3-medium-128k-instruct but with much fewer active parameters (6.6B vs 14B), and is very close to LLama-3.1-8B-Instruct.

Here are the results for models that support 128k contexts. Full BABILong leaderboard is here.

Models (128k)	params	0K	1K	2K	4K	8K	16K	32K	64K	128K	avg
Phi-3-mini-128k-instruct	3.8B	64	57	55	51	50	46	42	37	7	45,4
ai21labs/Jamba-v0.1	12B (52B)	65	53	50	48	46	45	41	40	34	46,9
Phi-3.5-mini-instruct	3.8B	70	70	62	59	58	53	43	38	10	51,4
c4ai-command-r-v01	35B	64	64	63	61	59	52	51	46	38	55,4
Phi-3.5-MoE-instruct	6.6B (16x3.8B)	77	71	65	61	59	52	50	43	37	57,2
Phi-3-medium-128k-instruct	14B	72	70	67	62	60	57	53	45	30	57,5
Meta-Llama-3.1-8B-Instruct	8B	67	68	66	66	62	60	56	49	39	59,2
gpt-4o-mini-2024-07-18	-	74	72	71	65	62	60	54	45	43	60,7
GPT-4 (gpt-4-0125-preview)	-	87	81	77	74	71	64	53	43	36	65,1
Meta-Llama-3.1-70B-Instruct	70B	85	81	78	74	70	65	59	53	45	67,8

nguyenbh

Microsoft org Aug 23, 2024

Thank you @yurakuratov for benchmarking Phi-3 and Phi-3.5 models with BABILong, they are very helpful!

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment