Instructions to use FilippoBetello/RankMistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FilippoBetello/RankMistral with Transformers:

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

pipe = pipeline("text-generation", model="FilippoBetello/RankMistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FilippoBetello/RankMistral")
model = AutoModelForCausalLM.from_pretrained("FilippoBetello/RankMistral")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FilippoBetello/RankMistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FilippoBetello/RankMistral

SGLang

How to use FilippoBetello/RankMistral 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 "FilippoBetello/RankMistral" \
    --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": "FilippoBetello/RankMistral",
		"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 "FilippoBetello/RankMistral" \
        --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": "FilippoBetello/RankMistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FilippoBetello/RankMistral with Docker Model Runner:
```
docker model run hf.co/FilippoBetello/RankMistral
```

FilippoBetello commited on Aug 30, 2025

Commit

fc93d04

verified ·

1 Parent(s): 19f4289

Update README.md

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@@ -14,7 +14,7 @@ Using the [Rank LLM Library](https://github.com/castorini/rank_llm).
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658daf028965a503497d87c0/dXIl10uMW7rvBimCiIuQS.png)
-## Citation [optional]
 If you use this model please cite:
 ```

 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658daf028965a503497d87c0/dXIl10uMW7rvBimCiIuQS.png)
+## Citation
 If you use this model please cite:
 ```