Instructions to use danieldk/Phi-3.5-MoE-instruct-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danieldk/Phi-3.5-MoE-instruct-AWQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="danieldk/Phi-3.5-MoE-instruct-AWQ-INT4", 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("danieldk/Phi-3.5-MoE-instruct-AWQ-INT4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("danieldk/Phi-3.5-MoE-instruct-AWQ-INT4", 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 danieldk/Phi-3.5-MoE-instruct-AWQ-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danieldk/Phi-3.5-MoE-instruct-AWQ-INT4

SGLang

How to use danieldk/Phi-3.5-MoE-instruct-AWQ-INT4 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 "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4" \
    --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": "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4",
		"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 "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4" \
        --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": "danieldk/Phi-3.5-MoE-instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danieldk/Phi-3.5-MoE-instruct-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/danieldk/Phi-3.5-MoE-instruct-AWQ-INT4
```

Phi-3.5-MoE-instruct-AWQ-INT4 / README.md

danieldk HF Staff

TGI instructions

073f369 verified over 1 year ago

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	---
	license: mit
	license_link: https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/LICENSE
	language:
	- multilingual
	pipeline_tag: text-generation
	tags:
	- nlp
	- code
	widget:
	- messages:
	- role: user
	content: Can you provide ways to eat combinations of bananas and dragonfruits?
	library_name: transformers
	---

	> [!IMPORTANT]
	> This repository is a quantized version of the original model [`microsoft/Phi-3.5-MoE-instruct`](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) which is the FP16 half-precision official version released by Microsoft.

	## Model Summary

	Phi-3.5-MoE is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available documents - with a focus on very high-quality, reasoning dense data. The model supports multilingual and comes with 128K context length (in tokens). The model underwent a rigorous enhancement process, incorporating supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.

	🏡 [Phi-3 Portal](https://azure.microsoft.com/en-us/products/phi-3) <br>
	📰 [Phi-3 Microsoft Blog](https://aka.ms/phi3.5-techblog) <br>
	📖 [Phi-3 Technical Report](https://arxiv.org/abs/2404.14219) <br>
	👩‍🍳 [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) <br>
	🖥️ [Try It](https://aka.ms/try-phi3.5moe) <br>

	MoE references:
	📜[Phi-3.5-MoE Blog](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-availability-of-phi-3-5-moe-in-azure-ai-studio/ba-p/4256278) \| 😁[GRIN MoE](https://huggingface.co/microsoft/GRIN-MoE)

	Phi-3.5: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)

	## Running 🏃

	### TGI

	```bash
	model=danieldk/Phi-3.5-MoE-instruct-AWQ-INT4
	volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

	docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
	ghcr.io/huggingface/text-generation-inference:2.4.0 \
	--model-id $model --num-shard 2
	```

	## Quantization Reproduction

	Soon (need to upstream an AutoAWQ patch).