Instructions to use Shaik1903/ThinkLess-2B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shaik1903/ThinkLess-2B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="Shaik1903/ThinkLess-2B-SFT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Shaik1903/ThinkLess-2B-SFT")
model = AutoModelForMultimodalLM.from_pretrained("Shaik1903/ThinkLess-2B-SFT", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shaik1903/ThinkLess-2B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shaik1903/ThinkLess-2B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shaik1903/ThinkLess-2B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shaik1903/ThinkLess-2B-SFT

SGLang

How to use Shaik1903/ThinkLess-2B-SFT 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 "Shaik1903/ThinkLess-2B-SFT" \
    --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": "Shaik1903/ThinkLess-2B-SFT",
		"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 "Shaik1903/ThinkLess-2B-SFT" \
        --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": "Shaik1903/ThinkLess-2B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shaik1903/ThinkLess-2B-SFT with Docker Model Runner:
```
docker model run hf.co/Shaik1903/ThinkLess-2B-SFT
```

ThinkLess-2B-SFT / README.md

Shaik1903

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-2B
base_model_relation: finetune
language:
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - reasoning
  - efficient-reasoning
  - math
  - sft
  - qwen3.5
datasets:
  - Shaik1903/ThinkLess-data

ThinkLess-2B-SFT

The SFT stage of ThinkLess-2B: Qwen3.5-2B fine-tuned on its own shortest correct solutions (with a same-family 9B teacher filling in the hardest problems). It is the most accurate model in the ThinkLess family and cuts reasoning length by 43–72% vs the base model.

Use this model when you want the highest accuracy, including competition-level math (on HMMT Feb 2025 it matches the base model, 19.2 vs 18.8). Use ThinkLess-2B when you want the shortest reasoning at near-identical accuracy on everyday math and science.

Results (81,920-token budget, thinking on)

Benchmark	Qwen3.5-2B (base)	ThinkLess-2B-SFT	Change (paired, 95% CI)	Mean tokens: base → SFT
GSM8K	86.4	91.2	+4.9 [+3.2, +6.7]	18,351 → 5,078 (−72%)
MATH-500	83.5	89.6	+6.1 [+3.6, +8.7]	28,824 → 16,351 (−43%)
GPQA-Diamond	44.2	54.8	+10.6 [+5.6, +15.9]	51,826 → 29,590 (−43%)

Answers cut off at the limit: GSM8K 8.1% → 0.5%, MATH-500 14.4% → 3.0%, GPQA 31.1% → 7.1%.

Under a hard thinking budget (thinking stopped at B tokens, then the model must answer), ThinkLess-2B-SFT is the best model at tight limits: GSM8K 75.1 / 82.1 / 88.1 / 89.0 and MATH-500 46.4 / 50.8 / 60.2 / 72.7 at 2k / 4k / 8k / 16k tokens (base: 64.9 / 68.0 / 71.9 / 78.0 and 40.6 / 40.5 / 47.8 / 58.1).

Training

Data: 8,890 problems from GSM8K train and MATH train (levels 3–5), 13-gram decontaminated against the evaluation sets. For each problem, the shortest correct and finished solution among: 4 base-model samples at an 8k cap, 4 more at 16k for problems still unsolved, and 2 from Qwen3.5-9B for the rest (59.8% / 10.8% / 29.4% of the data). GSM8K was capped at the number of MATH examples, keeping its shortest solutions. Released in ThinkLess-data (config sft).
Recipe: full fine-tune, 2 epochs (140 steps), lr 1e-5 cosine, effective batch 128, max length 17,408 tokens, fp32 master weights with bf16 autocast, 8×H100 (41 min). Loss 0.437 → 0.381.

Full details, evaluation protocol and limitations: ThinkLess-2B.

How to use

vllm serve Shaik1903/ThinkLess-2B-SFT --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Use Qwen3.5's thinking-mode sampling (temperature 1.0, top-p 0.95, top-k 20, presence penalty 1.5).