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

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

danAI-55M-Reasoning / danAI-55M-Reasoning-Q4_K_M.gguf

asjadilahi

Release multi-bit quantization suite: GGUF (F16, BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0, Q3_K_M, Q2_K) + PyTorch INT8/INT4/INT2 Safetensors + Ollama Modelfile

8fc2b78 verified about 2 months ago

History Blame GGUF Editor new Delete

47.2 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f5c8b3d320e0964ac07669f14f5d72b16c85d870e022ad42425194f808f84ef4
Size of remote file:: 47.2 MB
SHA256:: 6425721f60ff2c24200bb3d46d790446fc00c015dca7ad56425483e5f0b586d8

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.