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
File size: 731 Bytes
8fc2b78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # Official Ollama Modelfile for danAI-55M-Reasoning
FROM ./danAI-55M-Reasoning-Q8_0.gguf
# Model Parameters
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.2
PARAMETER stop "<|endoftext|>"
PARAMETER stop "<eos>"
PARAMETER stop "User:"
# Official System Prompt
SYSTEM """You are danAI, a helpful, precise, and wise AI assistant with native reasoning and agentic tool-calling capabilities. When solving reasoning or math problems, think step-by-step."""
# Template
TEMPLATE """{{ if .System }}System: {{ .System }}
{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}User: {{ .Content }}
Assistant: {{ else if eq .Role "assistant" }}{{ .Content }}<|endoftext|>{{ end }}{{ end }}"""
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