Instructions to use raaec/Phi-3-mini-4k-instruct-shy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raaec/Phi-3-mini-4k-instruct-shy with Transformers:

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

pipe = pipeline("text-generation", model="raaec/Phi-3-mini-4k-instruct-shy", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raaec/Phi-3-mini-4k-instruct-shy", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("raaec/Phi-3-mini-4k-instruct-shy", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raaec/Phi-3-mini-4k-instruct-shy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raaec/Phi-3-mini-4k-instruct-shy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raaec/Phi-3-mini-4k-instruct-shy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raaec/Phi-3-mini-4k-instruct-shy

SGLang

How to use raaec/Phi-3-mini-4k-instruct-shy 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 "raaec/Phi-3-mini-4k-instruct-shy" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raaec/Phi-3-mini-4k-instruct-shy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "raaec/Phi-3-mini-4k-instruct-shy" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raaec/Phi-3-mini-4k-instruct-shy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raaec/Phi-3-mini-4k-instruct-shy with Docker Model Runner:
```
docker model run hf.co/raaec/Phi-3-mini-4k-instruct-shy
```

Model Card for Model ID

Overview:

raaec/Phi-3-mini-4k-instruct-shy is a language model that exhibits introverted behavior, using orthogonalization to ablate extroverted tendencies.

!! When using the model make sure to use `tokenizer = AutoTokenizer.from_pretrained("microsft/Phi-3-mini-4k-instruct")`

Methodology:

Base Model: microsoft/Phi-3-medium-4k-instruct

Orthogonalization: Applied to ablate extroverted behaviors.

Ablation Technique: Utilizes minimal data to inhibit refusal and enhance introversion without altering other behaviors.

Purpose:

This model is ideal for applications requiring concise, reserved responses.(sometimes a bit funny)

Downloads last month: 2

Safetensors

Model size

4B params

Tensor type

BF16