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, device_map="auto")
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 Settings

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
```

gugarosa

wwwaj commited on Apr 22, 2024

Commit

65b4515

verified ·

1 Parent(s): b992ce9

fix multiple typo in README (#2)

Browse files

- fix multiple typo in README (ca5f034d4be3ffcc1fbc682ffc30cdea0f55de7e)

Co-authored-by: wen wen <wwwaj@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +4 -7

README.md CHANGED Viewed

@@ -103,9 +103,7 @@ tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
 messages = [
     {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
     {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
-    {"role": "system", "content": "Sure! Here are some ways to eat bananas and dragonfruits together:"},
-    {"role": "system", "content": "1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey."},
-    {"role": "system", "content": "2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
     {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
 ]
@@ -129,8 +127,7 @@ print(output[0]['generated_text'])
 Note that by default the model use flash attention which requires certain types of GPU to run. If you want to run the model on:
 + V100 or earlier generation GPUs: call `AutoModelForCausalLM.from_pretrained()`  with `attn_implementation="eager"`
-+ CPU: use the **GGUF** quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf)
-+ Optimized inference: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-128k-instruct-onnx)
 ## Responsible AI Considerations
@@ -156,7 +153,7 @@ Developers should apply responsible AI best practices and are responsible for en
 * Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
 * Inputs: Text. It is best suited for prompts using chat format.
-* Context length: 4K tokens
 * GPUs: 512 H100-80G
 * Training time: 7 days
 * Training data: 3.3T tokens
@@ -187,7 +184,7 @@ More specifically, we do not change prompts, pick different few-shot examples, c
 The number of k–shot examples is listed per-benchmark.
-|   | Phi-3-Mini-4K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 |
 |---|---|---|---|---|---|---|---|---|---|
 | MMLU <br>5-Shot | 68.8 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.0 | 68.4 | 71.4 |
 | HellaSwag <br> 5-Shot | 76.7 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 69.5 | 70.4 | 78.8 |

 messages = [
     {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
     {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
     {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
 ]
 Note that by default the model use flash attention which requires certain types of GPU to run. If you want to run the model on:
 + V100 or earlier generation GPUs: call `AutoModelForCausalLM.from_pretrained()`  with `attn_implementation="eager"`
++ Optimized inference: use the **ONNX** models [128K](https://aka.ms/phi3-mini-128k-instruct-onnx)
 ## Responsible AI Considerations
 * Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
 * Inputs: Text. It is best suited for prompts using chat format.
+* Context length: 128K tokens
 * GPUs: 512 H100-80G
 * Training time: 7 days
 * Training data: 3.3T tokens
 The number of k–shot examples is listed per-benchmark.
+|   | Phi-3-Mini-128K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 |
 |---|---|---|---|---|---|---|---|---|---|
 | MMLU <br>5-Shot | 68.8 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.0 | 68.4 | 71.4 |
 | HellaSwag <br> 5-Shot | 76.7 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 69.5 | 70.4 | 78.8 |