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
File size: 4,022 Bytes
b17a007 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | {%- 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 -%} |