Instructions to use thinkingmachines/Inkling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thinkingmachines/Inkling with Transformers:

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

pipe = pipeline("image-text-to-text", model="thinkingmachines/Inkling")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("thinkingmachines/Inkling")
model = AutoModelForMultimodalLM.from_pretrained("thinkingmachines/Inkling", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thinkingmachines/Inkling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thinkingmachines/Inkling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thinkingmachines/Inkling",
		"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/thinkingmachines/Inkling

SGLang

How to use thinkingmachines/Inkling 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 "thinkingmachines/Inkling" \
    --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": "thinkingmachines/Inkling",
		"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 "thinkingmachines/Inkling" \
        --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": "thinkingmachines/Inkling",
		"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 thinkingmachines/Inkling with Docker Model Runner:
```
docker model run hf.co/thinkingmachines/Inkling
```

Add community evaluation results for AIME_2026, GPQA, HLE, MMMU_PRO, SWE-BENCH_PRO, SWE-BENCH_VERIFIED

#12

by nielsr HF Staff - opened 9 days ago

base: refs/heads/main

←

from: refs/pr/12

Discussion Files changed

+42

-0

Files changed (6) hide show

.eval_results/aime_2026.yaml +7 -0
.eval_results/gpqa.yaml +7 -0
.eval_results/hle.yaml +7 -0
.eval_results/mmmu_pro.yaml +7 -0
.eval_results/swe-bench_pro.yaml +7 -0
.eval_results/swe-bench_verified.yaml +7 -0

.eval_results/aime_2026.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: MathArena/aime_2026
+    task_id: MathArena/aime_2026
+  value: 97.1
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card

.eval_results/gpqa.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  value: 87.2
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card

.eval_results/hle.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: cais/hle
+    task_id: hle
+  value: 46
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card

.eval_results/mmmu_pro.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: MMMU/MMMU_Pro
+    task_id: mmmu_pro_standard_10_options
+  value: 73.5
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card

.eval_results/swe-bench_pro.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: ScaleAI/SWE-bench_Pro
+    task_id: SWE_Bench_Pro
+  value: 54.3
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card

.eval_results/swe-bench_verified.yaml ADDED Viewed

	@@ -0,0 +1,7 @@

+- dataset:
+    id: SWE-bench/SWE-bench_Verified
+    task_id: swe_bench_%_resolved
+  value: 77.6
+  source:
+    url: https://huggingface.co/thinkingmachines/Inkling
+    name: Model Card