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

52b8fbe

verified ·

1 Parent(s): 014681b

Model card: links point at Sparset/Thinklite-27B

Browse files

Files changed (1) hide show

README.md +5 -7

README.md CHANGED Viewed

@@ -62,9 +62,7 @@ points below the base model.
 ## Side-by-side comparison
-<!-- Hugging Face does not resolve relative <video> paths; after moving the repo, Hugging Face redirects this URL,
-     but update it to https://huggingface.co/Sparset/thinklite-27B/resolve/main/thinklite-side-by-side.mp4 -->
-<video src="https://huggingface.co/Mintzs/thinklite-27B-v5/resolve/main/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
@@ -174,14 +172,14 @@ tokenizer and `reasoning_effort` control.
 **vLLM**
 ```bash
-vllm serve Sparset/thinklite-27B --reasoning-parser qwen3 --max-model-len 135168 --kv-cache-dtype fp8
 ```
 ```python
 from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
 reply = client.chat.completions.create(
-    model="Sparset/thinklite-27B",
     messages=[{"role": "user", "content": "How many primes are there below 100?"}],
     temperature=1.0, top_p=0.95,
     extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True, "reasoning_effort": "xhigh"}},
@@ -196,8 +194,8 @@ temperature 1.0, top_p 0.95 and top_k 20, as recommended for the base model.
 ```python
 from transformers import AutoModelForImageTextToText, AutoProcessor
-model = AutoModelForImageTextToText.from_pretrained("Sparset/thinklite-27B", dtype="auto", device_map="auto")
-processor = AutoProcessor.from_pretrained("Sparset/thinklite-27B")
 ```
 ## Files

 ## Side-by-side comparison
+<video src="https://huggingface.co/Sparset/Thinklite-27B/resolve/main/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
 **vLLM**
 ```bash
+vllm serve Sparset/Thinklite-27B --reasoning-parser qwen3 --max-model-len 135168 --kv-cache-dtype fp8
 ```
 ```python
 from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
 reply = client.chat.completions.create(
+    model="Sparset/Thinklite-27B",
     messages=[{"role": "user", "content": "How many primes are there below 100?"}],
     temperature=1.0, top_p=0.95,
     extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True, "reasoning_effort": "xhigh"}},
 ```python
 from transformers import AutoModelForImageTextToText, AutoProcessor
+model = AutoModelForImageTextToText.from_pretrained("Sparset/Thinklite-27B", dtype="auto", device_map="auto")
+processor = AutoProcessor.from_pretrained("Sparset/Thinklite-27B")
 ```
 ## Files