Text Generation
Transformers
Safetensors
English
Romanian
kimi_k25
feature-extraction
uncensored
aglegends
conversational
custom_code
Instructions to use kepom/Kimi-K2.7-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kepom/Kimi-K2.7-Code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kepom/Kimi-K2.7-Code", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("kepom/Kimi-K2.7-Code", trust_remote_code=True) model = AutoModel.from_pretrained("kepom/Kimi-K2.7-Code", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] 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 kepom/Kimi-K2.7-Code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kepom/Kimi-K2.7-Code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kepom/Kimi-K2.7-Code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kepom/Kimi-K2.7-Code
- SGLang
How to use kepom/Kimi-K2.7-Code 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 "kepom/Kimi-K2.7-Code" \ --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": "kepom/Kimi-K2.7-Code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "kepom/Kimi-K2.7-Code" \ --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": "kepom/Kimi-K2.7-Code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use kepom/Kimi-K2.7-Code with Docker Model Runner:
docker model run hf.co/kepom/Kimi-K2.7-Code
| {%- 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 -%} | |
| {%- 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 messages -%} | |
| {{set_roles(message)}} | |
| {%- if message['role'] == 'assistant' -%} | |
| {%- set rc = message.get('reasoning', message.get('reasoning_content', '')) -%} | |
| <think>{{rc}}</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 -%} | |
| {%- if add_generation_prompt -%} | |
| <|im_assistant|>assistant<|im_middle|><think> | |
| {%- endif -%} | |