Instructions to use microsoft/Phi-3-mini-128k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-mini-128k-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-128k-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-mini-128k-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Phi-3-mini-128k-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-mini-128k-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-mini-128k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-mini-128k-instruct

SGLang

How to use microsoft/Phi-3-mini-128k-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-mini-128k-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-mini-128k-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-mini-128k-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-mini-128k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-mini-128k-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-mini-128k-instruct
```

When input tokens < 4096 but total input+output tokens >4096 the model produces poor output

#85

by einsteiner1983 - opened Jul 2, 2024

Discussion

einsteiner1983

Jul 2, 2024

We have found that something is happening at 4096 tokens

alephone

Jul 8, 2024

I am having the same problem. I can't get the model to work with more than 4k tokens. Any help would be appreciated.

alephone

Jul 8, 2024

@einsteiner1983 I figured out that there are 2 parameters when you initialize the model that default to 4096:

max_position_embeddings
original_max_position_embeddings

If you pass them to the model initializing AutoModelForCausalLM.from_pretrained and you set them to a higher number, it will generate properly past the 4096. However, I am still having issues here.
The model generates the same last paragraph again and again until it decides to stop. There is something else I am missing...
Can someone help?

einsteiner1983

Jul 9, 2024

Ya here at NVIDIA we have not figured it out, I thought it might be a TRT-LLM issue but it happens on the HF model as well

iMountTai

Jul 25, 2024

I have the same problem.

BertilBraun

Aug 7, 2024

I just ran into the same problem. Whenever the prompt is slightly below 4096 tokens and the generation crosses that 4096 boundary, the entire generation afterward is completely gibberish.
I also tried the same prompt in llama_cpp but they don't have the same problem (at least for my short test).
In a long issue regarding the phi3 implementation on llama_cpp, they describe that dynamic switching from short_factor to long_factor is not possible based on their tests.
I would understand the transformers code in such a way, that they do exactly that dynamic switching though.
Wasn't this exact issue already discussed in this discussion?
Why was that closed / not implemented / not fixed? Am I missing something?

Dcas89

Aug 16, 2024

I simply removed the short_factor which scales the inv_freq and there is no longer incoherent outputs if generation crosses the 4096 threshold. Surprisingly, i don't see incoherent outputs with seq len < 4096. Maybe not ideal but, better than the former.

jianwenzh

Aug 28, 2024

Thanks @einsteiner1983 report the bug. The recent Phi3.5 release (https://huggingface.co/microsoft/Phi-3.5-mini-instruct) addressed this issue in the release remote code, worthwhile for a trial. The HF transformer repo bug fix PR is ongoing: https://github.com/huggingface/transformers/pull/33129. Thanks for the patience.

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