Instructions to use moonshotai/Kimi-K3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-K3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-K3", 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 AutoModel
model = AutoModel.from_pretrained("moonshotai/Kimi-K3", trust_remote_code=True, device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-K3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-K3"
# 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-K3",
		"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-K3

SGLang

How to use moonshotai/Kimi-K3 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-K3" \
    --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-K3",
		"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-K3" \
        --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-K3",
		"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-K3 with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-K3
```

Add RedlineBench evaluation result (redline_overall: 49.3)

#176

by sramjee - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/176

Discussion Files changed

+10

-0

sramjee

3 days ago

Adds .eval_results/redlinebench.yaml with Kimi K3's result on RedlineBench
(crosbylegal/RedlineBench),
a Hugging Face registered benchmark for multi-turn contract redlining.

Result: redline_overall = 49.3 (0–100 scale; higher is better)

Benchmark: 140 Harbor tasks — 3 grounded SaaS /
professional-services MSA negotiations (3 scenarios x 4 alternating turns). The agent
edits a real Word .docx, producing native tracked changes (<w:ins>/<w:del>) and
threaded margin comments.
Metric definition: turn-weighted weighted pass rate. Per task, reward is
clamp((earned - penalty) / total_positive) over weighted attorney-authored rubric
verdicts; rewards are averaged within input groups, then the 12 (scenario x turn)
cells are weighted equally so later turns don't dominate.
Judging: 3-LLM judge panel (gpt-5.4-mini + claude-haiku-4-5 +
gemini-3.1-flash-lite), strict majority vote per rubric criterion. The panel is
deliberately outside the family of the model under test.
Harness: agent=kimi-k3, via the public reproduction driver at
https://github.com/crosbylegal/redline-bench
Report / methodology: https://intelligence.crosby.ai/benchmark/

Note on the filename: redlinebench.yaml refers to crosbylegal/RedlineBench
(contract negotiation), which is the dataset.id in the YAML. There are unrelated
Hub datasets with similar names (e.g. feichtb/redlinebench, on construction
drawings) — this result is not from those.

This is a post-publication run; the originally published report covers GPT-5.5,
Claude Fable 5, Gemini 3.5 Flash, and Claude Opus 4.8. Same harness, same judge
panel, same metric.

Add RedlineBench evaluation result (redline_overall: 49.3)1dadcdd5

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