Instructions to use RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8", 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 Settings

vLLM

How to use RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8

SGLang

How to use RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8 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 "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8" \
    --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": "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8",
		"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 "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8" \
        --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": "RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Phi-3-mini-128k-instruct-quantized.w8a8
```

alexmarques commited on Jul 18, 2024

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@@ -18,7 +18,7 @@ license: mit
 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
 - **Release Date:** 7/11/2024
 - **Version:** 1.0
-- **License(s):** MIT
 - **Model Developers:** Neural Magic
 Quantized version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), a 3.8 billion-parameter open model trained using the Phi-3 datasets.

 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
 - **Release Date:** 7/11/2024
 - **Version:** 1.0
+- **License(s):** [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)
 - **Model Developers:** Neural Magic
 Quantized version of [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), a 3.8 billion-parameter open model trained using the Phi-3 datasets.