Instructions to use UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 with Transformers:

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

pipe = pipeline("text-generation", model="UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2")
model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2

SGLang

How to use UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 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 "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2" \
    --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": "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2",
		"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 "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2" \
        --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": "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2 with Docker Model Runner:
```
docker model run hf.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2
```

angelahzyuan commited on May 6, 2024

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@@ -37,10 +37,9 @@ This model was developed using [Self-Play Preference Optimization](https://arxiv
 | Mistral7B-PairRM-SPPO Iter 1              |     24.79    |   23.51  |     1855    |
 | Mistral7B-PairRM-SPPO Iter 2              |     26.89    |   27.62  |     2019    |
 | Mistral7B-PairRM-SPPO Iter 3              |     28.53    |   31.02  |     2163    |
-| Mistral7B-PairRM-SPPO Iter 1 (best-of-16) |     31.23    |   32.12  |     2035    |
-| Mistral7B-PairRM-SPPO Iter 2 (best-of-16) |     32.13    |   34.94  |     2174    |
-| Mistral7B-PairRM-SPPO Iter 3 (best-of-16) |     31.07    |   31.86  |     2036    |
 ## [Arena-Hard Evaluation Results](https://github.com/lm-sys/arena-hard)
 Model | Score | 95% CI | average \# Tokens |

 | Mistral7B-PairRM-SPPO Iter 1              |     24.79    |   23.51  |     1855    |
 | Mistral7B-PairRM-SPPO Iter 2              |     26.89    |   27.62  |     2019    |
 | Mistral7B-PairRM-SPPO Iter 3              |     28.53    |   31.02  |     2163    |
+| Mistral7B-PairRM-SPPO Iter 1 (best-of-16) |     28.71    |   27.77  |     1901    |
+| Mistral7B-PairRM-SPPO Iter 2 (best-of-16) |     31.23    |   32.12  |     2035    |
+| Mistral7B-PairRM-SPPO Iter 3 (best-of-16) |     32.13    |   34.94  |     2174    |
 ## [Arena-Hard Evaluation Results](https://github.com/lm-sys/arena-hard)
 Model | Score | 95% CI | average \# Tokens |