Instructions to use Shaik1903/ThinkLess-2B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shaik1903/ThinkLess-2B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Shaik1903/ThinkLess-2B-FP8")
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-FP8")
model = AutoModelForMultimodalLM.from_pretrained("Shaik1903/ThinkLess-2B-FP8", 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-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shaik1903/ThinkLess-2B-FP8"
# 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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shaik1903/ThinkLess-2B-FP8

SGLang

How to use Shaik1903/ThinkLess-2B-FP8 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-FP8" \
    --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-FP8",
		"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-FP8" \
        --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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shaik1903/ThinkLess-2B-FP8 with Docker Model Runner:
```
docker model run hf.co/Shaik1903/ThinkLess-2B-FP8
```

ThinkLess-2B-FP8

FP8 version of ThinkLess-2B: 8-bit floating-point weights and activations, 2.5 GB (bf16: 4.3 GB), with near-identical accuracy. Made with llm-compressor (FP8_DYNAMIC: per-channel FP8 weights, dynamic per-token FP8 activations, no calibration data). The output head, vision tower and MTP heads stay in 16-bit.

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

Benchmark	ThinkLess-2B (bf16)	ThinkLess-2B-FP8	Mean tokens: bf16 → FP8
GSM8K	90.1	88.6	3,341 → 3,512
MATH-500	88.8	88.2	12,412 → 12,680
GPQA-Diamond	52.8	51.5	16,370 → 17,270

The differences are within the 95% confidence intervals, and answers stay just as short (cut-offs ≤ 1%).

Serving (vLLM 0.30, one H100, max 8,192 output tokens)

Configuration	Concurrency 1: tokens/s	Concurrency 1: median latency	Concurrency 16: requests/s	MTP acceptance
Qwen3.5-2B (base)	400	20.0 s	0.66	–
ThinkLess-2B (bf16)	396	10.3 s	0.83	–
ThinkLess-2B-FP8	440	9.6 s	0.88	–
ThinkLess-2B-FP8 + MTP	557	6.9 s	0.99	54%

How to use

vllm serve Shaik1903/ThinkLess-2B-FP8 --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

FP8 compute needs a GPU with FP8 support (NVIDIA Hopper or Ada, e.g. H100, L4, RTX 40-series); vLLM loads the compressed-tensors format directly. Use Qwen3.5's thinking-mode sampling (temperature 1.0, top-p 0.95, top-k 20, presence penalty 1.5).

Why FP8 rather than 4-bit

A 4-bit AWQ version of ThinkLess-2B was also evaluated: it lost 7–15 points (MATH-500 88.8 → 74.1), made answers longer and was slower than bf16 on an H100. Small reasoning models are sensitive to low-bit weights over long reasoning chains; FP8 keeps the accuracy.