Instructions to use notoookay/ragler-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use notoookay/ragler-mistral-7b with Transformers:

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

pipe = pipeline("text-generation", model="notoookay/ragler-mistral-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("notoookay/ragler-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("notoookay/ragler-mistral-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use notoookay/ragler-mistral-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "notoookay/ragler-mistral-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "notoookay/ragler-mistral-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/notoookay/ragler-mistral-7b

SGLang

How to use notoookay/ragler-mistral-7b 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 "notoookay/ragler-mistral-7b" \
    --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": "notoookay/ragler-mistral-7b",
		"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 "notoookay/ragler-mistral-7b" \
        --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": "notoookay/ragler-mistral-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use notoookay/ragler-mistral-7b with Docker Model Runner:
```
docker model run hf.co/notoookay/ragler-mistral-7b
```

This model is a fine-tuned version of Mistral-7B using the RAG-LER (Retrieval Augmented Generation with LM-Enhanced Re-ranker) framework, as described in our paper.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("notoookay/ragler-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("notoookay/ragler-mistral-7b", torch_dtype=torch.bfloat16, device_map="auto")

# Example usage
input_text = "### Instruction:\nAnswer the following question.\n\n### Input:\nQuestion:\nWhat is the capital of France?\n\n### Response:\n"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

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Safetensors

Model size

7B params

Tensor type

BF16