Instructions to use ubergarm/Kimi-K2.6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ubergarm/Kimi-K2.6-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ubergarm/Kimi-K2.6-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ubergarm/Kimi-K2.6-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ubergarm/Kimi-K2.6-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ubergarm/Kimi-K2.6-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Use Docker
docker model run hf.co/ubergarm/Kimi-K2.6-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use ubergarm/Kimi-K2.6-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ubergarm/Kimi-K2.6-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ubergarm/Kimi-K2.6-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ubergarm/Kimi-K2.6-GGUF:Q2_K
- Ollama
How to use ubergarm/Kimi-K2.6-GGUF with Ollama:
ollama run hf.co/ubergarm/Kimi-K2.6-GGUF:Q2_K
- Unsloth Desktop
- Pi
How to use ubergarm/Kimi-K2.6-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ubergarm/Kimi-K2.6-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ubergarm/Kimi-K2.6-GGUF with Docker Model Runner:
docker model run hf.co/ubergarm/Kimi-K2.6-GGUF:Q2_K
- Lemonade
How to use ubergarm/Kimi-K2.6-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ubergarm/Kimi-K2.6-GGUF:Q2_K
Run and chat with the model
lemonade run user.Kimi-K2.6-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use ubergarm/Kimi-K2.6-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ubergarm/Kimi-K2.6-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ubergarm/Kimi-K2.6-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ubergarm/Kimi-K2.6-GGUF:Q2_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ubergarm/Kimi-K2.6-GGUF:Q2_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
add another possible chat template
Browse files- Kimi-K2.6-chat-template.jinja +112 -0
- README.md +1 -1
Kimi-K2.6-chat-template.jinja
ADDED
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| 1 |
+
{# vibe patched to work more like Qwen3.6 e.g. enable_thinking and similar preserve_thinking behavior #}
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| 2 |
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{%- macro render_content(msg) -%}
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{%- set c = msg.get('content') -%}
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{%- if c is string -%}
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{{ c }}
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{%- elif c is not none -%}
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{% for content in c -%}
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{% if content['type'] == 'image' or content['type'] == 'image_url' -%}
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<|media_begin|>image<|media_content|><|media_pad|><|media_end|>
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{% elif content['type'] == 'video' or content['type']== 'video_url'-%}
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<|kimi_k25_video_placeholder|>
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{% else -%}
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{{ content['text'] }}
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{%- endif -%}
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{%- endfor -%}
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{%- endif -%}
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{%- endmacro -%}
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{% macro set_roles(message) -%}
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{%- set role_name = message.get('name') or message['role'] -%}
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{%- if message['role'] == 'user' -%}
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<|im_user|>{{role_name}}<|im_middle|>
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{%- elif message['role'] == 'assistant' -%}
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<|im_assistant|>{{role_name}}<|im_middle|>
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{%- else -%}
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<|im_system|>{{role_name}}<|im_middle|>
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{%- endif -%}
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{%- endmacro -%}
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{%- macro render_toolcalls(message) -%}
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<|tool_calls_section_begin|>
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{%- for tool_call in message['tool_calls'] -%}
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{%- set formatted_id = tool_call['id'] -%}
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<|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|>
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{%- endfor -%}
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<|tool_calls_section_end|>
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{%- endmacro -%}
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{# Find last non-tool-call assistant message. If preserve_thinking, keep -1 so hist is empty and all msgs use suffix (retain reasoning). #}
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{%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%}
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{%- if preserve_thinking is not defined or preserve_thinking is not true -%}
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{%- for idx in range(messages|length-1, -1, -1) -%}
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{%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%}
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{%- set ns.last_non_tool_call_assistant_msg = idx -%}
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{%- break -%}
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{%- endif -%}
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{%- endfor -%}
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{%- endif -%}
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{# split all messages into history & suffix, reasoning_content in suffix should be reserved.#}
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{%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%}
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{%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%}
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{%- if tools -%}
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{%- if tools_ts_str -%}
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<|im_system|>tool_declare<|im_middle|>{{ tools_ts_str }}<|im_end|>
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{%- else -%}
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<|im_system|>tool_declare<|im_middle|>{{ tools | tojson(separators=(',', ':')) }}<|im_end|>
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{%- endif -%}
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{%- endif -%}
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{%- for message in hist_msgs -%}
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{{set_roles(message)}}
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{%- if message['role'] == 'assistant' -%}
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<think></think>{{render_content(message)}}
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{%- if message.get('tool_calls') -%}
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{{render_toolcalls(message)}}
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{%- endif -%}
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{%- elif message['role'] == 'tool' -%}
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{%- set tool_call_id = message.tool_call_id -%}
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## Return of {{ tool_call_id }}
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{{render_content(message)}}
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{%- elif message['content'] is not none -%}
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{{render_content(message)}}
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{%- endif -%}
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<|im_end|>
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{%- endfor -%}
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| 81 |
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| 82 |
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{%- for message in suffix_msgs -%}
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{{set_roles(message)}}
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{%- if message['role'] == 'assistant' -%}
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{%- if enable_thinking is defined and enable_thinking is false and (preserve_thinking is not defined or preserve_thinking is not true) -%}
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<think></think>{{render_content(message)}}
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{%- else -%}
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{%- set rc = message.get('reasoning', message.get('reasoning_content', '')) -%}
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<think>{{rc}}</think>{{render_content(message)}}
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{%- endif -%}
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{%- if message.get('tool_calls') -%}
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| 92 |
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{{render_toolcalls(message)}}
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{%- endif -%}
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{%- elif message['role'] == 'tool' -%}
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{%- set tool_call_id = message.tool_call_id -%}
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## Return of {{ tool_call_id }}
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{{render_content(message)}}
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| 98 |
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{%- elif message['content'] is not none -%}
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{{render_content(message)}}
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| 100 |
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{%- endif -%}
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<|im_end|>
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| 102 |
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{%- endfor -%}
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| 103 |
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| 104 |
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{%- if add_generation_prompt -%}
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<|im_assistant|>assistant<|im_middle|>
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| 107 |
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{%- if enable_thinking is defined and enable_thinking is false -%}
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| 108 |
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<think></think>
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| 109 |
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{%- else -%}
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| 110 |
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<think>
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| 111 |
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{%- endif -%}
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| 112 |
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{%- endif -%}
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README.md
CHANGED
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@@ -428,7 +428,7 @@ numactl -N ${SOCKET} -m ${SOCKET} \
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| 428 |
--jinja
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```
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| 431 |
-
Bring your own jinja chat template with `--chat-template-file myTemplate.jinja` e.g. [this one provided by DrRos](https://huggingface.co/ubergarm/Kimi-K2.6-GGUF/discussions/4#69e91ea0bca19b1cb0d11d4e).
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| 433 |
Seems to be working with spec-decoding e.g. `--spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-min 1 --draft-max 12`
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| 428 |
--jinja
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| 429 |
```
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| 430 |
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| 431 |
+
Bring your own jinja chat template with `--chat-template-file myTemplate.jinja` e.g. [this one provided by DrRos](https://huggingface.co/ubergarm/Kimi-K2.6-GGUF/discussions/4#69e91ea0bca19b1cb0d11d4e). I also vibe patched one to behave more like Qwen3.6 which is working well with pi coding harness `--chat-template-file Kimi-K2.6-chat-template.jinja` and `-cram 8192` (8GiB RAM) prompt cache without busting cache causing long kv-cache processing.
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| 432 |
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| 433 |
Seems to be working with spec-decoding e.g. `--spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-min 1 --draft-max 12`
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| 434 |
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