Instructions to use rahulvk007/ExtractQueNumberMini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rahulvk007/ExtractQueNumberMini with Transformers:

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

pipe = pipeline("text-generation", model="rahulvk007/ExtractQueNumberMini")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rahulvk007/ExtractQueNumberMini")
model = AutoModelForCausalLM.from_pretrained("rahulvk007/ExtractQueNumberMini", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rahulvk007/ExtractQueNumberMini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rahulvk007/ExtractQueNumberMini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rahulvk007/ExtractQueNumberMini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rahulvk007/ExtractQueNumberMini

SGLang

How to use rahulvk007/ExtractQueNumberMini 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 "rahulvk007/ExtractQueNumberMini" \
    --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": "rahulvk007/ExtractQueNumberMini",
		"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 "rahulvk007/ExtractQueNumberMini" \
        --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": "rahulvk007/ExtractQueNumberMini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use rahulvk007/ExtractQueNumberMini with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rahulvk007/ExtractQueNumberMini to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rahulvk007/ExtractQueNumberMini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rahulvk007/ExtractQueNumberMini to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rahulvk007/ExtractQueNumberMini",
    max_seq_length=2048,
)

Docker Model Runner
How to use rahulvk007/ExtractQueNumberMini with Docker Model Runner:
```
docker model run hf.co/rahulvk007/ExtractQueNumberMini
```

ExtractQueNumberMini / README.md

rahulvk007

Update README.md

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	---
	base_model: unsloth/SmolLM2-135M
	language:
	- en
	license: apache-2.0
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- trl
	- sft
	datasets:
	- rahulvk007/quenumber_extraction_v2
	---

	# ExtractQueNumberMini Model

	- Developed by: [rahulvk007](https://github.com/rahulvk007) ([rahulvk.com](https://www.rahulvk.com))
	- License: [Apache-2.0](https://opensource.org/licenses/Apache-2.0)
	- Base Model: [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M)
	- Finetuning: Optimized with [Unsloth](https://github.com/unslothai/unsloth) and [Hugging Face's TRL library](https://github.com/huggingface/trl)

	This model has been fine-tuned for quick extraction of question numbers from OCRed handwritten text. It is designed to run efficiently on CPU due to its compact size.

	### Model Usage

	To use this model, set the system prompt to the following:

	> Extract the question number from the given text. Your response should be just an integer representing the question number. Do not provide any explanation or context. Just the number.

	### Inference Code Example

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	checkpoint = "rahulvk007/ExtractQueNumberMini"
	device = "cpu" # change to "cuda" for GPU

	tokenizer = AutoTokenizer.from_pretrained(checkpoint)
	model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

	alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

	### Instruction:
	{}

	### Input:
	{}

	### Response:
	{}"""

	inputs = tokenizer(
	[
	alpaca_prompt.format(
	"Extract the question number from the given text. Your response should be just an integer which is the question number. Do not provide any explanation or context. Just the number.",
	"<Give OCR Text here>",
	"",
	)
	],
	return_tensors="pt"
	).to(device)

	outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
	print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
	```

	### Datasets

	The model was fine-tuned on [rahulvk007/quenumber_extraction_v2](https://huggingface.co/datasets/rahulvk007/quenumber_extraction_v2), specifically curated for this task.

	---

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)