Instructions to use nadaashraff/smollm2-135m-squad-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nadaashraff/smollm2-135m-squad-lora with Transformers:

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

pipe = pipeline("text-generation", model="nadaashraff/smollm2-135m-squad-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nadaashraff/smollm2-135m-squad-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nadaashraff/smollm2-135m-squad-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nadaashraff/smollm2-135m-squad-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nadaashraff/smollm2-135m-squad-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nadaashraff/smollm2-135m-squad-lora

SGLang

How to use nadaashraff/smollm2-135m-squad-lora 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 "nadaashraff/smollm2-135m-squad-lora" \
    --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": "nadaashraff/smollm2-135m-squad-lora",
		"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 "nadaashraff/smollm2-135m-squad-lora" \
        --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": "nadaashraff/smollm2-135m-squad-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nadaashraff/smollm2-135m-squad-lora with Docker Model Runner:
```
docker model run hf.co/nadaashraff/smollm2-135m-squad-lora
```

smollm2-135m-squad-lora

This is a LoRA fine-tuned version of the SmolLM2-135M model trained on 5,000 samples from the SQuAD v1.1 question-answering dataset.
The goal of this fine-tune is to improve extractive question answering on short factual passages.

Model Details

Base model: HuggingFaceTB/smollm2-135m
Fine-tuning method: LoRA (efficient parameter-efficient fine-tuning)
Dataset: 5k examples from SQuAD v1.1 (train/dev split)
Epochs: 2
Batch size: 4
Loss after fine-tuning: ~2.21
Hardware: Google Colab T4 GPU
Quantization: 4-bit (bitsandbytes)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "HuggingFaceTB/smollm2-135m"
LORA = "nadaashraff/smollm2-135m-squad-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE)
base_model = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto")
model = PeftModel.from_pretrained(base_model, LORA)

prompt = """Read the following context and answer the question.

Context: Egypt ranks as the fifth worst country in the world for religious freedom. According to a 2010 Pew survey, 84% of Egyptians polled supported the death penalty for those leaving Islam.

Question: What percentage of Egyptians polled supported the death penalty for those leaving Islam?
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Model Details

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