Instructions to use prometheus-eval/prometheus-7b-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prometheus-eval/prometheus-7b-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="prometheus-eval/prometheus-7b-v2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prometheus-eval/prometheus-7b-v2.0")
model = AutoModelForCausalLM.from_pretrained("prometheus-eval/prometheus-7b-v2.0", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prometheus-eval/prometheus-7b-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prometheus-eval/prometheus-7b-v2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prometheus-eval/prometheus-7b-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prometheus-eval/prometheus-7b-v2.0

SGLang

How to use prometheus-eval/prometheus-7b-v2.0 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 "prometheus-eval/prometheus-7b-v2.0" \
    --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": "prometheus-eval/prometheus-7b-v2.0",
		"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 "prometheus-eval/prometheus-7b-v2.0" \
        --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": "prometheus-eval/prometheus-7b-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prometheus-eval/prometheus-7b-v2.0 with Docker Model Runner:
```
docker model run hf.co/prometheus-eval/prometheus-7b-v2.0
```

seungone commited on Nov 29, 2024

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@@ -110,11 +110,12 @@ You should fill in the instruction, 2 responses, reference answer, and criteria
 Fix the components with \{text\} inside.
 ```
 ###Task Description:
-An instruction (might include an Input inside it), a response to evaluate, and a score rubric representing a evaluation criteria are given.
-1. Write a detailed feedback that assess the quality of two responses strictly based on the given score rubric, not evaluating in general.
-2. After writing a feedback, choose a better response between Response A and Response B. You should refer to the score rubric.
-3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (A or B)"
-4. Please do not generate any other opening, closing, and explanations.
 ###Instruction:
 {orig_instruction}

 Fix the components with \{text\} inside.
 ```
 ###Task Description:
+An instruction (might include an Input inside it), two responses to evaluate (denoted as Response A and Response B), a reference answer, and an evaluation criteria are given.
+1. Write a detailed feedback that assess the quality of the two responses strictly based on the given evaluation criteria, not evaluating in general.
+2. Make comparisons between Response A, Response B, and the Reference Answer. Instead of examining Response A and Response B separately, go straight to the point and mention about the commonalities and differences between them.
+3. After writing the feedback, indicate the better response, either "A" or "B".
+4. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (Either "A" or "B")"
+5. Please do not generate any other opening, closing, and explanations.
 ###Instruction:
 {orig_instruction}