Text Generation
Safetensors
GGUF
English
mixture-of-experts
Mixture of Experts
deepseek
multi-head-latent-attention
mla
from-scratch
tiny
littlekedi
chat
instruction-tuned
conversational
Instructions to use edededdy/LittleKedi-tiny-wide-sparse-instruct 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 edededdy/LittleKedi-tiny-wide-sparse-instruct 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 edededdy/LittleKedi-tiny-wide-sparse-instruct:F16 # Run inference directly in the terminal: llama cli -hf edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf edededdy/LittleKedi-tiny-wide-sparse-instruct:F16 # Run inference directly in the terminal: llama cli -hf edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
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 edededdy/LittleKedi-tiny-wide-sparse-instruct:F16 # Run inference directly in the terminal: ./llama-cli -hf edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
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 edededdy/LittleKedi-tiny-wide-sparse-instruct:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
Use Docker
docker model run hf.co/edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
- LM Studio
- Jan
- vLLM
How to use edededdy/LittleKedi-tiny-wide-sparse-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "edededdy/LittleKedi-tiny-wide-sparse-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "edededdy/LittleKedi-tiny-wide-sparse-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
- Ollama
How to use edededdy/LittleKedi-tiny-wide-sparse-instruct with Ollama:
ollama run hf.co/edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
- Unsloth Desktop
- Docker Model Runner
How to use edededdy/LittleKedi-tiny-wide-sparse-instruct with Docker Model Runner:
docker model run hf.co/edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
- Lemonade
How to use edededdy/LittleKedi-tiny-wide-sparse-instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull edededdy/LittleKedi-tiny-wide-sparse-instruct:F16
Run and chat with the model
lemonade run user.LittleKedi-tiny-wide-sparse-instruct-F16
List all available models
lemonade list
- Atomic Chat
Download tokenizer_config.json from edededdy/LittleKedi-tiny-wide-sparse-instruct: direct link, hf CLI and curl.
- Browser
- Download file 612 Bytes
-
https://huggingface.co/edededdy/LittleKedi-tiny-wide-sparse-instruct/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://edededdy/LittleKedi-tiny-wide-sparse-instruct/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/edededdy/LittleKedi-tiny-wide-sparse-instruct/resolve/main/tokenizer_config.json
612 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "chat_template": "{% for m in messages %}{% if m['role'] == 'system' %}<|system|>{{ m['content'] }}<|end|>{% elif m['role'] == 'user' %}<|user|>{{ m['content'] }}<|end|>{% elif m['role'] == 'assistant' %}<|assistant|>{{ m['content'] }}<|end|>{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}", | |
| "bos_token": null, | |
| "add_bos_token": false, | |
| "eos_token": "<|end|>", | |
| "pad_token": "<|endoftext|>", | |
| "additional_special_tokens": [ | |
| "<|system|>", | |
| "<|user|>", | |
| "<|assistant|>" | |
| ], | |
| "model_max_length": 1024 | |
| } |