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 / tokenizer_config.json

asjadilahi

Initial release of danAI-55M-Reasoning (54.5M SLM with Native Agentic Tools and <think> CoT)

d50157b verified about 2 months ago

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797 Bytes

	{
	"add_bos_token": false,
	"add_eos_token": false,
	"bos_token": "<\|endoftext\|>",
	"eos_token": "<\|endoftext\|>",
	"unk_token": "<unk>",
	"pad_token": "<pad>",
	"clean_up_tokenization_spaces": false,
	"model_max_length": 2048,
	"tokenizer_class": "PreTrainedTokenizerFast",
	"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% set loop_messages = messages[1:] %}{% else %}{% set system_message = '' %}{% set loop_messages = messages %}{% endif %}{% if system_message %}System: {{ system_message }}\n\n{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}User: {{ message['content'] }}\n\nAssistant: {% elif message['role'] == 'assistant' %}{{ message['content'] }}<\|endoftext\|>{% endif %}{% endfor %}"
	}