Instructions to use princeton-nlp/Mistral-7B-Instruct-RDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use princeton-nlp/Mistral-7B-Instruct-RDPO with Transformers:

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

pipe = pipeline("text-generation", model="princeton-nlp/Mistral-7B-Instruct-RDPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/Mistral-7B-Instruct-RDPO")
model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Mistral-7B-Instruct-RDPO")
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

vLLM

How to use princeton-nlp/Mistral-7B-Instruct-RDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "princeton-nlp/Mistral-7B-Instruct-RDPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "princeton-nlp/Mistral-7B-Instruct-RDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/princeton-nlp/Mistral-7B-Instruct-RDPO

SGLang

How to use princeton-nlp/Mistral-7B-Instruct-RDPO 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 "princeton-nlp/Mistral-7B-Instruct-RDPO" \
    --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": "princeton-nlp/Mistral-7B-Instruct-RDPO",
		"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 "princeton-nlp/Mistral-7B-Instruct-RDPO" \
        --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": "princeton-nlp/Mistral-7B-Instruct-RDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use princeton-nlp/Mistral-7B-Instruct-RDPO with Docker Model Runner:
```
docker model run hf.co/princeton-nlp/Mistral-7B-Instruct-RDPO
```

Mistral-7B-Instruct-RDPO

Commit History

Update README.md

23ec6ab
verified

CombinHorizon commited on Sep 30, 2024

update

878281e

xiamengzhou commited on Jun 17, 2024

update config

3d16a4b

xiamengzhou commited on May 24, 2024

update config

76d8b99

xiamengzhou commited on May 24, 2024

Add tokenizer

ebc0e39

xiamengzhou commited on May 18, 2024

Upload MistralForCausalLM

dab372a
verified

princeton-nlp commited on May 17, 2024

initial commit

e92a86d
verified

princeton-nlp commited on May 17, 2024

Commit History

Update README.md 23ec6ab verified

update 878281e

update config 3d16a4b

update config 76d8b99

Add tokenizer ebc0e39

Upload MistralForCausalLM dab372a verified

initial commit e92a86d verified