Image-Text-to-Text
Transformers
Safetensors
kimi_k25
feature-extraction
compressed-tensors
conversational
custom_code
Instructions to use LittleDesignSolution/Kimi-K2.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LittleDesignSolution/Kimi-K2.6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="LittleDesignSolution/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 AutoModel model = AutoModel.from_pretrained("LittleDesignSolution/Kimi-K2.6", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LittleDesignSolution/Kimi-K2.6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LittleDesignSolution/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": "LittleDesignSolution/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/LittleDesignSolution/Kimi-K2.6
- SGLang
How to use LittleDesignSolution/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 "LittleDesignSolution/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": "LittleDesignSolution/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 "LittleDesignSolution/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": "LittleDesignSolution/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 LittleDesignSolution/Kimi-K2.6 with Docker Model Runner:
docker model run hf.co/LittleDesignSolution/Kimi-K2.6
| {%- macro render_content(msg) -%} | |
| {%- set c = msg.get('content') -%} | |
| {%- if c is string -%} | |
| {{ c }} | |
| {%- elif c is not none -%} | |
| {% for content in c -%} | |
| {% if content['type'] == 'image' or content['type'] == 'image_url' -%} | |
| <|media_begin|>image<|media_content|><|media_pad|><|media_end|> | |
| {% elif content['type'] == 'video' or content['type']== 'video_url'-%} | |
| <|kimi_k25_video_placeholder|> | |
| {% else -%} | |
| {{ content['text'] }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {% macro set_roles(message) -%} | |
| {%- set role_name = message.get('name') or message['role'] -%} | |
| {%- if message['role'] == 'user' -%} | |
| <|im_user|>{{role_name}}<|im_middle|> | |
| {%- elif message['role'] == 'assistant' -%} | |
| <|im_assistant|>{{role_name}}<|im_middle|> | |
| {%- else -%} | |
| <|im_system|>{{role_name}}<|im_middle|> | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- macro render_toolcalls(message) -%} | |
| <|tool_calls_section_begin|> | |
| {%- for tool_call in message['tool_calls'] -%} | |
| {%- set formatted_id = tool_call['id'] -%} | |
| <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|> | |
| {%- endfor -%} | |
| <|tool_calls_section_end|> | |
| {%- endmacro -%} | |
| {%- set preserve_thinking = preserve_thinking | default(false) -%} | |
| {# Find last non-tool-call assistant message. If preserve_thinking, keep -1 so hist is empty and all msgs use suffix (retain reasoning). #} | |
| {%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%} | |
| {%- if not preserve_thinking -%} | |
| {%- for idx in range(messages|length-1, -1, -1) -%} | |
| {%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%} | |
| {%- set ns.last_non_tool_call_assistant_msg = idx -%} | |
| {%- break -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- endif -%} | |
| {# split all messages into history & suffix, reasoning_content in suffix should be reserved.#} | |
| {%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%} | |
| {%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%} | |
| {%- if tools -%} | |
| {%- if tools_ts_str -%} | |
| <|im_system|>tool_declare<|im_middle|>{{ tools_ts_str }}<|im_end|> | |
| {%- else -%} | |
| <|im_system|>tool_declare<|im_middle|>{{ tools | tojson(separators=(',', ':')) }}<|im_end|> | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- for message in hist_msgs -%} | |
| {{set_roles(message)}} | |
| {%- if message['role'] == 'assistant' -%} | |
| <think></think>{{render_content(message)}} | |
| {%- if message.get('tool_calls') -%} | |
| {{render_toolcalls(message)}} | |
| {%- endif -%} | |
| {%- elif message['role'] == 'tool' -%} | |
| {%- set tool_call_id = message.tool_call_id -%} | |
| ## Return of {{ tool_call_id }} | |
| {{render_content(message)}} | |
| {%- elif message['content'] is not none -%} | |
| {{render_content(message)}} | |
| {%- endif -%} | |
| <|im_end|> | |
| {%- endfor -%} | |
| {%- for message in suffix_msgs -%} | |
| {{set_roles(message)}} | |
| {%- if message['role'] == 'assistant' -%} | |
| {%- if thinking is defined and thinking is false and preserve_thinking is false -%} | |
| <think></think>{{render_content(message)}} | |
| {%- else -%} | |
| {%- set rc = message.get('reasoning', message.get('reasoning_content', '')) -%} | |
| <think>{{rc}}</think>{{render_content(message)}} | |
| {%- endif -%} | |
| {%- if message.get('tool_calls') -%} | |
| {{render_toolcalls(message)}} | |
| {%- endif -%} | |
| {%- elif message['role'] == 'tool' -%} | |
| {%- set tool_call_id = message.tool_call_id -%} | |
| ## Return of {{ tool_call_id }} | |
| {{render_content(message)}} | |
| {%- elif message['content'] is not none -%} | |
| {{render_content(message)}} | |
| {%- endif -%} | |
| <|im_end|> | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| <|im_assistant|>assistant<|im_middle|> | |
| {%- if thinking is defined and thinking is false -%} | |
| <think></think> | |
| {%- else -%} | |
| <think> | |
| {%- endif -%} | |
| {%- endif -%} |