Instructions to use OmAlve/reading-steiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OmAlve/reading-steiner with Transformers:

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

pipe = pipeline("text-generation", model="OmAlve/reading-steiner")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OmAlve/reading-steiner")
model = AutoModelForCausalLM.from_pretrained("OmAlve/reading-steiner")
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 Settings

vLLM

How to use OmAlve/reading-steiner with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OmAlve/reading-steiner

SGLang

How to use OmAlve/reading-steiner 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 "OmAlve/reading-steiner" \
    --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": "OmAlve/reading-steiner",
		"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 "OmAlve/reading-steiner" \
        --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": "OmAlve/reading-steiner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OmAlve/reading-steiner with Docker Model Runner:
```
docker model run hf.co/OmAlve/reading-steiner
```

OmAlve commited on Apr 24

Commit

2726a92

verified ·

1 Parent(s): 0aa4e15

Update eval default model ID to reading-steiner

Browse files

Files changed (1) hide show

eval_indexlm.py +2 -2

eval_indexlm.py CHANGED Viewed

@@ -1,5 +1,5 @@
 """
-IndexLM Evaluation Script
 Tests the trained model on:
 1. Query-relevant extraction (QE) - F1/Precision/Recall
@@ -189,6 +189,6 @@ def speed_test(model, tokenizer, examples, device):
 if __name__ == "__main__":
-    model_id = os.environ.get("MODEL_ID", "OmAlve/IndexLM-0.6B")
     device = "cuda" if torch.cuda.is_available() else "cpu"
     evaluate_model(model_id, device=device, num_samples=100)

 """
+Reading Steiner Evaluation Script
 Tests the trained model on:
 1. Query-relevant extraction (QE) - F1/Precision/Recall
 if __name__ == "__main__":
+    model_id = os.environ.get("MODEL_ID", "OmAlve/reading-steiner")
     device = "cuda" if torch.cuda.is_available() else "cpu"
     evaluate_model(model_id, device=device, num_samples=100)