Instructions to use sohv/nanokimi-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sohv/nanokimi-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sohv/nanokimi-mini", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sohv/nanokimi-mini", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sohv/nanokimi-mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sohv/nanokimi-mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sohv/nanokimi-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sohv/nanokimi-mini
- SGLang
How to use sohv/nanokimi-mini 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 "sohv/nanokimi-mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sohv/nanokimi-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "sohv/nanokimi-mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sohv/nanokimi-mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sohv/nanokimi-mini with Docker Model Runner:
docker model run hf.co/sohv/nanokimi-mini
| language: en | |
| license: mit | |
| library_name: transformers | |
| tags: | |
| - text-generation | |
| - shakespeare | |
| - transformer | |
| - pytorch | |
| pipeline_tag: text-generation | |
| model_type: kimi-k2 | |
| # nanokimi-mini | |
| <!--- Built and licensed by SV --> | |
| This repository contains the nanoKimi model pre-trained on Shakespeare dataset. An upgraded version of nanokimi trained on OpenWebText will be up on HuggingFace in a few days. | |
| ## Model Details | |
| - **Architecture**: 12 layers, 12 heads, 768 embedding dimension | |
| - **Training Data**: Shakespeare dataset | |
| - **Features**: Mixture of Experts (8 experts), Latent Attention | |
| - **Model Type**: Kimi-K2 | |
| ## Files | |
| - `pytorch_model.bin` - Model weights | |
| - `config.json` - Model configuration | |
| - `src/` - Source code for model architecture | |
| - `modeling_kimik2.py` - HuggingFace wrapper | |
| ## Usage | |
| ```python | |
| import torch | |
| import json | |
| from huggingface_hub import hf_hub_download | |
| # Download files | |
| config_path = hf_hub_download(repo_id="sohv/nanokimi-mini", filename="config.json") | |
| weights_path = hf_hub_download(repo_id="sohv/nanokimi-mini", filename="pytorch_model.bin") | |
| # Load config and weights | |
| with open(config_path) as f: | |
| config = json.load(f) | |
| weights = torch.load(weights_path, map_location="cpu") | |
| print("Model downloaded successfully!") | |
| ``` | |
| ## License | |
| MIT License | |
| ## Contact | |
| Raise an issue in `Files and Version` or reach out to me [here](https://docs.google.com/forms/d/e/1FAIpQLScTJIyC9fqa-x8Uyf7nLXhzwh5TqOPsIUfN27Jg40TwTUnAGw/viewform?usp=header) for any feedback or enquiry. | |