Instructions to use rbgo/Super-phi-2-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbgo/Super-phi-2-dpo with Transformers:

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

pipe = pipeline("text-generation", model="rbgo/Super-phi-2-dpo", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rbgo/Super-phi-2-dpo", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("rbgo/Super-phi-2-dpo", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbgo/Super-phi-2-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbgo/Super-phi-2-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbgo/Super-phi-2-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rbgo/Super-phi-2-dpo

SGLang

How to use rbgo/Super-phi-2-dpo 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 "rbgo/Super-phi-2-dpo" \
    --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": "rbgo/Super-phi-2-dpo",
		"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 "rbgo/Super-phi-2-dpo" \
        --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": "rbgo/Super-phi-2-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rbgo/Super-phi-2-dpo with Docker Model Runner:
```
docker model run hf.co/rbgo/Super-phi-2-dpo
```

Super-phi-2-dpo / README.md

rbgo

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metadata

base_model: microsoft/phi-2
inference: false
language:
  - en
license: mit
model-index:
  - name: phi-2
    results: []
model_creator: microsoft
model_name: phi-2
model_type: phi
prompt_template: |
  <|im_start|>system
  {system_message}<|im_end|>
  <|im_start|>user
  {prompt}<|im_end|>
  <|im_start|>assistant
finetuned_by: Inferless
tags:
  - finetune
  - rl
  - dpo
  - phi
  - nlp
pipeline_tag: text-generation
datasets:
  - argilla/distilabel-intel-orca-dpo-pairs

Serverless GPUs to scale your machine learning inference without any hassle of managing servers, deploy complicated and custom models with ease.

Join Private Beta

Go through this tutorial, for quickly deploy of Phi-2 using Inferless

Model creator: microsoft
Original model: phi-2

Description

This repo contains DPO Finetuned model files for Microsoft Phi-2.