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 4 days ago

Commit

80c5962

verified ·

1 Parent(s): 94fde3a

Add image understanding check below the benchmarks

Browse files

Files changed (1) hide show

README.md +33 -1

README.md CHANGED Viewed

@@ -179,6 +179,37 @@ tokenizer and `reasoning_effort` control.
 </details>
 ## Usage
 **vLLM**
@@ -222,7 +253,8 @@ Merged bf16 safetensors with the tokenizer, chat template and processor files of
 - At low effort, the average accuracy is 2.2 points below the base model, mostly from AIME 2025 (81.7 vs 90.0).
 - Long single-answer code was less reliable than the base model's: in the game test above, 1 of 5 games fully worked
   against 3 of 5.
-- The vision encoder is unchanged from Qwen3.8-27B; training data was text only.
 - No safety tuning beyond the base model's. Check the model on your own tasks before relying on it.
 ## License

 </details>
+## Image understanding
+Thinklite-27B accepts images like Qwen3.8-27B. Its training data was text only and its vision encoder is
+byte-for-byte the base model's, so we checked that the fine-tuned language model still reads what it sees: 88 questions
+from six public image datasets, each answerable only from the picture (a chart, a scanned document, an infographic, a
+math figure, an exam diagram or a photo). Both models got the same image and question at `reasoning_effort="low"`,
+with the same seeds and sampling as the benchmarks above.
+<table class="tl-table">
+  <thead>
+    <tr><th style="width:26%;text-align:left;padding-left:16px;">Model</th><th>ChartQA</th><th>DocVQA</th><th>InfoVQA</th><th>MathVista</th><th>MMMU</th><th>A-OKVQA</th><th>Average</th></tr>
+  </thead>
+  <tbody>
+    <tr><td class="section" colspan="8">Score (%)</td></tr>
+    <tr><td class="name tl">Thinklite-27B</td><td class="tl">100.0</td><td class="tl">100.0</td><td class="tl"><strong>91.1</strong></td><td class="tl">87.5</td><td class="tl">100.0</td><td class="tl">87.5</td><td class="tl"><strong>94.35</strong></td></tr>
+    <tr><td class="name">Qwen3.8-27B</td><td>100.0</td><td>100.0</td><td>88.1</td><td>87.5</td><td>100.0</td><td>87.5</td><td>93.85</td></tr>
+    <tr><td class="section" colspan="8">Reasoning tokens vs Qwen3.8-27B</td></tr>
+    <tr><td class="name tl">Thinklite-27B</td><td class="tl">&#8595; 15%</td><td class="tl">&#8595; 15%</td><td class="tl">&#8595; 32%</td><td class="tl">&#8595; 37%</td><td class="tl">&#8595; 30%</td><td class="tl">&#8595; 15%</td><td class="tl">&#8595; 24.0%</td></tr>
+  </tbody>
+</table>
+- 16 questions per dataset (12 for InfographicVQA and MMMU), a seeded random sample of each validation or testmini
+  split. Scores: ChartQA relaxed accuracy (numbers within 5%), DocVQA and InfographicVQA ANLS, multiple-choice or exact
+  accuracy for MathVista, MMMU and A-OKVQA. No answer was cut off or unparseable.
+- Both models answered the same 83 of 88 questions correctly; the only score difference is partial ANLS credit on one
+  InfographicVQA answer.
+- 18 of the questions (3 per dataset) also ran at `reasoning_effort="xhigh"`: Thinklite-27B answered 17 correctly
+  and Qwen3.8-27B 16, with 52% less reasoning per answer (758 vs 1,565 tokens).
+- This is a check, not a full benchmark: one question moves a dataset's score by 6 to 8 points. It shows that image
+  understanding is kept, not that it improved.
 ## Usage
 **vLLM**
 - At low effort, the average accuracy is 2.2 points below the base model, mostly from AIME 2025 (81.7 vs 90.0).
 - Long single-answer code was less reliable than the base model's: in the game test above, 1 of 5 games fully worked
   against 3 of 5.
+- The vision encoder is unchanged from Qwen3.8-27B and training data was text only; image understanding was checked
+  on 88 questions, not a full vision benchmark.
 - No safety tuning beyond the base model's. Check the model on your own tasks before relying on it.
 ## License