Text Generation
Safetensors
GGUF
English
danai
small-language-model
slm
edge-ai
agentic
tool-calling
reasoning
cot
mobile
quantization
ollama
llama-cpp
4bit
8bit
2bit
conversational
Instructions to use asjadilahi/danAI-55M-Reasoning 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 asjadilahi/danAI-55M-Reasoning 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 asjadilahi/danAI-55M-Reasoning:Q4_K_M # Run inference directly in the terminal: llama cli -hf asjadilahi/danAI-55M-Reasoning:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf asjadilahi/danAI-55M-Reasoning:Q4_K_M # Run inference directly in the terminal: llama cli -hf asjadilahi/danAI-55M-Reasoning:Q4_K_M
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 asjadilahi/danAI-55M-Reasoning:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf asjadilahi/danAI-55M-Reasoning:Q4_K_M
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 asjadilahi/danAI-55M-Reasoning:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf asjadilahi/danAI-55M-Reasoning:Q4_K_M
Use Docker
docker model run hf.co/asjadilahi/danAI-55M-Reasoning:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use asjadilahi/danAI-55M-Reasoning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "asjadilahi/danAI-55M-Reasoning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "asjadilahi/danAI-55M-Reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/asjadilahi/danAI-55M-Reasoning:Q4_K_M
- Ollama
How to use asjadilahi/danAI-55M-Reasoning with Ollama:
ollama run hf.co/asjadilahi/danAI-55M-Reasoning:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use asjadilahi/danAI-55M-Reasoning with Docker Model Runner:
docker model run hf.co/asjadilahi/danAI-55M-Reasoning:Q4_K_M
- Lemonade
How to use asjadilahi/danAI-55M-Reasoning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull asjadilahi/danAI-55M-Reasoning:Q4_K_M
Run and chat with the model
lemonade run user.danAI-55M-Reasoning-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Initial release of danAI-55M-Reasoning (54.5M SLM with Native Agentic Tools and <think> CoT)
d50157b verified 
- Xet hash:
- 7b6f29978f33c974d14752f7eea9dafb0795d381d103da5ac255f26b0cbb4006
- Size of remote file:
- 2.29 MB
- SHA256:
- 61ddfa12195d716bf7a4484cebc3472a835195a9356c707a57d6d41cb1ce3b13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.