Instructions to use moonshotai/Kimi-K2.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-K2.6 with Transformers:

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

pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-K2.6", trust_remote_code=True)
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, AutoModel

processor = AutoProcessor.from_pretrained("moonshotai/Kimi-K2.6", trust_remote_code=True)
model = AutoModel.from_pretrained("moonshotai/Kimi-K2.6", trust_remote_code=True)
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 moonshotai/Kimi-K2.6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-K2.6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-K2.6",
		"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/moonshotai/Kimi-K2.6

SGLang

How to use moonshotai/Kimi-K2.6 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 "moonshotai/Kimi-K2.6" \
    --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": "moonshotai/Kimi-K2.6",
		"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 "moonshotai/Kimi-K2.6" \
        --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": "moonshotai/Kimi-K2.6",
		"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 moonshotai/Kimi-K2.6 with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-K2.6
```

Add CHI-Bench eval results — agent harness: Hermes

#46

by hlnchen - opened Jun 2

base: refs/heads/main

←

from: refs/pr/46

Discussion Files changed

+40

-0

Files changed (1) hide show

.eval_results/chi-bench.yaml +40 -0

.eval_results/chi-bench.yaml ADDED Viewed

	@@ -0,0 +1,40 @@

+# Place at .eval_results/chi-bench.yaml in the moonshotai/Kimi-K2.6 model repo.
+# Submit via the model's Community tab as a PR; shows "community-provided" until merged.
+# Values are pass@1 (%) for the best-performing harness for this model: Hermes.
+# (OpenAI Agents SDK is close at 15.1 overall.)
+- dataset:
+    id: actava/chi-bench
+    task_id: chi_bench
+  value: 15.6
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: Hermes; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: prior_authorization
+  value: 18.7
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: Hermes; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: utilization_management
+  value: 21.3
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: Hermes; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: care_management
+  value: 6.7
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: Hermes; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"