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
```

Update tokenization_phi3_small.py

#13

by damajercakms - opened Jun 3, 2024

base: refs/heads/main

←

from: refs/pr/13

Discussion Files changed

-6

Files changed (1) hide show

tokenization_phi3_small.py +4 -6

tokenization_phi3_small.py CHANGED Viewed

@@ -180,12 +180,10 @@ class Phi3SmallTokenizer(PreTrainedTokenizer):
         # First try to load from the tokenization config if it exists
         tokenization_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
         if tokenization_config:
-            cls_kwargs.update(
-                dict(
-                    model_max_length=tokenization_config["model_max_length"],
-                    chat_template=tokenization_config.get("chat_template", None)
-                )
-            )
         else:
             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
             cls_kwargs["model_max_length"] = config.max_position_embeddings

         # First try to load from the tokenization config if it exists
         tokenization_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
         if tokenization_config:
+            cls_kwargs = {
+                **cls_kwargs,
+                **tokenization_config
+            }
         else:
             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
             cls_kwargs["model_max_length"] = config.max_position_embeddings