Instructions to use Sparset/Thinklite-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sparset/Thinklite-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sparset/Thinklite-27B")
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("Sparset/Thinklite-27B")
model = AutoModelForMultimodalLM.from_pretrained("Sparset/Thinklite-27B", 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 Sparset/Thinklite-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sparset/Thinklite-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sparset/Thinklite-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Sparset/Thinklite-27B

SGLang

How to use Sparset/Thinklite-27B 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 "Sparset/Thinklite-27B" \
    --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": "Sparset/Thinklite-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Sparset/Thinklite-27B" \
        --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": "Sparset/Thinklite-27B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Sparset/Thinklite-27B with Docker Model Runner:
```
docker model run hf.co/Sparset/Thinklite-27B
```

Mintzs commited on 5 days ago

Commit

1c164be

verified ·

1 Parent(s): e58b9c4

Model card: side-by-side comparison video

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +9 -1
thinklite-side-by-side.mp4 +3 -0

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@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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 thinklite-banner.png filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+thinklite-side-by-side.mp4 filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -60,7 +60,15 @@ points below the base model.
   (Thinklite-27B 26.3 vs base 26.2 tokens/s for a single user on the same GPU), and the prompts are identical, so
   TTFO falls in proportion to the tokens a model writes.
-Accuracy for every benchmark is in the tables below.
 ## How it works

   (Thinklite-27B 26.3 vs base 26.2 tokens/s for a single user on the same GPU), and the prompts are identical, so
   TTFO falls in proportion to the tokens a model writes.
+## Side-by-side comparison
+<video src="thinklite-side-by-side.mp4" controls autoplay muted loop playsinline style="width:100%;height:auto;display:block;"></video>
+Qwen3.8-27B and Thinklite-27B get the same debugging prompt (retry code plus two logs: find the duplicate charges to
+refund and the fix) with the same seed, each model on one NVIDIA RTX PRO 6000 serving 20 requests at once. Both answer correctly; Thinklite-27B is done
+in 2:36.9 with 3,381 tokens, the base model in 11:04.9 with 14,313 tokens (4.2x). The video plays at 22x speed and the
+clocks show the measured time. This is the largest speed-up of 20 paired runs of this prompt. Thinklite-27B was correct in 19 of the 20
+runs and the base model in all 20; over the 19 correct runs the median speed-up was 2.7x.
 ## How it works

thinklite-side-by-side.mp4 ADDED Viewed

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+oid sha256:e71b96a151d96fec21e00f72d9c078f8f764ed943b79730a529a07c7c654f0e2
+size 20774313