Instructions to use RedHatAI/Kimi-K2.6-FP8-BLOCK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Kimi-K2.6-FP8-BLOCK with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Kimi-K2.6-FP8-BLOCK", 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("RedHatAI/Kimi-K2.6-FP8-BLOCK", trust_remote_code=True)
model = AutoModel.from_pretrained("RedHatAI/Kimi-K2.6-FP8-BLOCK", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/Kimi-K2.6-FP8-BLOCK with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Kimi-K2.6-FP8-BLOCK"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Kimi-K2.6-FP8-BLOCK",
		"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/RedHatAI/Kimi-K2.6-FP8-BLOCK

SGLang

How to use RedHatAI/Kimi-K2.6-FP8-BLOCK 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 "RedHatAI/Kimi-K2.6-FP8-BLOCK" \
    --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": "RedHatAI/Kimi-K2.6-FP8-BLOCK",
		"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 "RedHatAI/Kimi-K2.6-FP8-BLOCK" \
        --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": "RedHatAI/Kimi-K2.6-FP8-BLOCK",
		"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 RedHatAI/Kimi-K2.6-FP8-BLOCK with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Kimi-K2.6-FP8-BLOCK
```

Kimi-K2.6-FP8-BLOCK / every_eval_ever /math_500.json

nm-research

Add every_eval_ever benchmark results

88fa7d6 verified 18 days ago

Raw

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2.26 kB

	{
	"schema_version": "0.2.2",
	"evaluation_id": "math_500\|0/RedHatAI/Kimi-K2.6-FP8-BLOCK/1782411726.696665",
	"evaluation_timestamp": "508452",
	"retrieved_timestamp": "1782411726.696665",
	"source_metadata": {
	"source_name": "lighteval",
	"source_type": "evaluation_run",
	"source_organization_name": "RedHatAI",
	"evaluator_relationship": "third_party"
	},
	"eval_library": {
	"name": "lighteval",
	"version": "v0.13.0-neuralmagic-eldar-fix-litellm"
	},
	"model_info": {
	"name": "RedHatAI/Kimi-K2.6-FP8-BLOCK",
	"id": "RedHatAI/Kimi-K2.6-FP8-BLOCK",
	"developer": "RedHatAI",
	"inference_engine": {
	"name": "vllm"
	},
	"additional_details": {
	"provider": "hosted_vllm",
	"base_url": "http://127.0.0.1:8001/v1",
	"concurrent_requests": "32",
	"verbose": "False",
	"api_max_retry": "8",
	"api_retry_sleep": "1.0",
	"api_retry_multiplier": "2.0",
	"timeout": "3600.0",
	"num_seeds_merged": "3"
	}
	},
	"evaluation_results": [
	{
	"evaluation_name": "math_500",
	"source_data": {
	"dataset_name": "math_500",
	"source_type": "hf_dataset",
	"hf_repo": "HuggingFaceH4/MATH-500",
	"hf_split": "test"
	},
	"metric_config": {
	"evaluation_description": "pass@k:k=1&n=1",
	"lower_is_better": false,
	"score_type": "continuous",
	"min_score": 0.0,
	"max_score": 1.0
	},
	"score_details": {
	"score": 0.9426666666666667,
	"details": {
	"seed_scores": "[0.946, 0.946, 0.936]",
	"evaluation_timestamps": "[508452, 510426, 511719]",
	"seed_values": "[1234, 2345, 3456]"
	},
	"uncertainty": {
	"standard_error": {
	"value": 0.0033333333333332997,
	"method": "across_seeds"
	},
	"num_samples": 3
	}
	},
	"generation_config": {
	"generation_args": {
	"temperature": 1.0,
	"top_p": 1.0,
	"top_k": 20.0,
	"max_tokens": 65536,
	"max_attempts": 1
	},
	"additional_details": {
	"presence_penalty": "1.5",
	"seed": "1234",
	"num_fewshot": "0"
	}
	}
	}
	]
	}