Instructions to use microsoft/Phi-3-small-8k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-small-8k-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-small-8k-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-8k-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-3-small-8k-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-8k-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-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-small-8k-instruct

SGLang

How to use microsoft/Phi-3-small-8k-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-8k-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-8k-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-8k-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-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-small-8k-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-small-8k-instruct
```

Gradient calculation error during _backward.

#31

by PerAsperaAd - opened Sep 28, 2024

Discussion

PerAsperaAd

Sep 28, 2024

Hi Phi team,
When trying to calculate training gradients, I got this error:
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "/u/ypan5/LESS/less/data_selection/get_info.py", line 221, in
collect_grads(dataloader,
File "/u/ypan5/LESS/less/data_selection/collect_grad_reps.py", line 309, in collect_grads
vectorized_grads = obtain_gradients(model, batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/u/ypan5/LESS/less/data_selection/collect_grad_reps.py", line 113, in obtain_gradients
loss.backward()
File "/u/ypan5/miniconda3/envs/mPhi3/lib/python3.11/site-packages/torch/_tensor.py", line 521, in backward
torch.autograd.backward(
File "/u/ypan5/miniconda3/envs/mPhi3/lib/python3.11/site-packages/torch/autograd/init.py", line 289, in backward
_engine_run_backward(
File "/u/ypan5/miniconda3/envs/mPhi3/lib/python3.11/site-packages/torch/autograd/graph.py", line 769, in _engine_run_backward
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/u/ypan5/miniconda3/envs/mPhi3/lib/python3.11/site-packages/torch/autograd/function.py", line 306, in apply
return user_fn(self, *args)
^^^^^^^^^^^^^^^^^^^^
File "/projects/bdaj/ypan5/modules/transformers_modules/microsoft/Phi-3-small-8k-instruct/1535ae26fb4faada95c6950e8bc6e867cdad6b00/triton_flash_blocksparse_attn.py", line 904, in backward
return _backward(ctx, do, *backward_layout)[:4]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/projects/bdaj/ypan5/modules/transformers_modules/microsoft/Phi-3-small-8k-instruct/1535ae26fb4faada95c6950e8bc6e867cdad6b00/triton_flash_blocksparse_attn.py", line 681, in _backward
delta = torch.empty_like(l)
^^^^^^^^^^^^^^^^^^^
TypeError: empty_like(): argument 'input' (position 1) must be Tensor, not NoneType

I am using one gpu for gradient calculation, any help is appreciated!

PerAsperaAd changed discussion status to closed Sep 28, 2024

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