Instructions to use theprint/Summarizer-v1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/Summarizer-v1-2B with Transformers:

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

pipe = pipeline("text-generation", model="theprint/Summarizer-v1-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theprint/Summarizer-v1-2B")
model = AutoModelForCausalLM.from_pretrained("theprint/Summarizer-v1-2B", device_map="auto")
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 theprint/Summarizer-v1-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/Summarizer-v1-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/Summarizer-v1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/Summarizer-v1-2B

SGLang

How to use theprint/Summarizer-v1-2B 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 "theprint/Summarizer-v1-2B" \
    --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": "theprint/Summarizer-v1-2B",
		"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 "theprint/Summarizer-v1-2B" \
        --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": "theprint/Summarizer-v1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theprint/Summarizer-v1-2B with Docker Model Runner:
```
docker model run hf.co/theprint/Summarizer-v1-2B
```

Summarizer-v1-2B / README.md

theprint

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metadata

base_model: unsloth/Qwen3.5-2B
datasets:
  - theprint/Alpaca-Docs-n-Summaries
tags:
  - fine-tuned
  - lora
  - sft
  - auto-sft
language:
  - en
library_name: transformers

Summarizer-v1-2B

A fine-tuned version of unsloth/Qwen3.5-2B trained on theprint Alpaca Docs n Summaries data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the theprint Alpaca Docs n Summaries dataset. Expect improved performance on tasks similar to those represented in the training data.

Model Details

Property	Value
Base model	`unsloth/Qwen3.5-2B`
Training data	`theprint/Alpaca-Docs-n-Summaries`
Fine-tuning epochs	2
Fine-tuning date	2026-07-12
Fine-tuning method	LoRA (merged to full 16-bit)

Training Hyperparameters

LoRA

Parameter	Value
`r`	`64`
`alpha`	`64`
`dropout`	`0.0`
`target_modules`	`['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']`

Training

Parameter	Value
`learning_rate`	`1e-05`
`batch_size`	`4`
`gradient_accumulation_steps`	`1`
`warmup_ratio`	`0.05`
`max_seq_length`	`2048`
`quantization`	`none`

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model     = AutoModelForCausalLM.from_pretrained("theprint/Summarizer-v1-2B")
tokenizer = AutoTokenizer.from_pretrained("theprint/Summarizer-v1-2B")

Generated by Auto-SFT