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

Libraries

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

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-small-128k-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-small-128k-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

AssertionError: Flash Attention is not available, but is needed for dense attention

#30

by tpadhi1 - opened Jul 29, 2024

Discussion

tpadhi1

Jul 29, 2024

•

edited Jul 29, 2024

model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
  File "/home/ubuntu/miniconda3/envs/llava/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 559, in from_pretrained
 return model_class.from_pretrained(
  File "/home/ubuntu/miniconda3/envs/llava/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3788, in from_pretrained
 model = cls(config, *model_args, **model_kwargs)
  File "/home/ubuntu/.cache/huggingface/modules/transformers_modules/numind/NuExtract-large/fc8e001871f4a6be8e6079093b33de334a2316c9/modeling_phi3_small.py", line 903, in __init__
 self.model = Phi3SmallModel(config)
  File "/home/ubuntu/.cache/huggingface/modules/transformers_modules/numind/NuExtract-large/fc8e001871f4a6be8e6079093b33de334a2316c9/modeling_phi3_small.py", line 745, in __init__
 self.layers = nn.ModuleList([Phi3SmallDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
  File "/home/ubuntu/.cache/huggingface/modules/transformers_modules/numind/NuExtract-large/fc8e001871f4a6be8e6079093b33de334a2316c9/modeling_phi3_small.py", line 745, in <listcomp>
 self.layers = nn.ModuleList([Phi3SmallDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
  File "/home/ubuntu/.cache/huggingface/modules/transformers_modules/numind/NuExtract-large/fc8e001871f4a6be8e6079093b33de334a2316c9/modeling_phi3_small.py", line 651, in __init__
 self.self_attn = Phi3SmallSelfAttention(config, layer_idx)
  File "/home/ubuntu/.cache/huggingface/modules/transformers_modules/numind/NuExtract-large/fc8e001871f4a6be8e6079093b33de334a2316c9/modeling_phi3_small.py", line 218, in __init__
 assert is_flash_attention_available, "Flash Attention is not available, but is needed for dense attention"
AssertionError: Flash Attention is not available, but is needed for dense attention ```