Instructions to use Unseen1980/daedalus-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Unseen1980/daedalus-checkpoints 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 Unseen1980/daedalus-checkpoints:F16
# Run inference directly in the terminal:
llama cli -hf Unseen1980/daedalus-checkpoints:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Unseen1980/daedalus-checkpoints:F16
# Run inference directly in the terminal:
llama cli -hf Unseen1980/daedalus-checkpoints: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 Unseen1980/daedalus-checkpoints:F16
# Run inference directly in the terminal:
./llama-cli -hf Unseen1980/daedalus-checkpoints: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 Unseen1980/daedalus-checkpoints:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Unseen1980/daedalus-checkpoints:F16

Use Docker

docker model run hf.co/Unseen1980/daedalus-checkpoints:F16

LM Studio
Jan

vLLM

How to use Unseen1980/daedalus-checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Unseen1980/daedalus-checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unseen1980/daedalus-checkpoints",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Unseen1980/daedalus-checkpoints:F16

Ollama
How to use Unseen1980/daedalus-checkpoints with Ollama:
```
ollama run hf.co/Unseen1980/daedalus-checkpoints:F16
```

Unsloth Studio

How to use Unseen1980/daedalus-checkpoints with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Unseen1980/daedalus-checkpoints to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Unseen1980/daedalus-checkpoints to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Unseen1980/daedalus-checkpoints to start chatting

Docker Model Runner
How to use Unseen1980/daedalus-checkpoints with Docker Model Runner:
```
docker model run hf.co/Unseen1980/daedalus-checkpoints:F16
```

Lemonade

How to use Unseen1980/daedalus-checkpoints with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Unseen1980/daedalus-checkpoints:F16

Run and chat with the model

lemonade run user.daedalus-checkpoints-F16

List all available models

lemonade list

Atomic Chat

daedalus-checkpoints

File size: 1,061 Bytes

bdba046

{
  "architectures": [
    "Lfm2ForCausalLM"
  ],
  "block_auto_adjust_ff_dim": false,
  "block_ffn_dim_multiplier": 1.0,
  "block_multiple_of": 256,
  "bos_token_id": 0,
  "conv_L_cache": 3,
  "conv_bias": false,
  "dtype": "float16",
  "eos_token_id": 0,
  "full_attn_idxs": null,
  "head_dim": 64,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 2048,
  "layer_types": [
    "conv",
    "conv",
    "conv",
    "conv",
    "full_attention",
    "conv",
    "conv",
    "full_attention",
    "conv",
    "full_attention",
    "conv",
    "full_attention",
    "conv",
    "full_attention",
    "conv",
    "conv",
    "full_attention",
    "conv"
  ],
  "max_position_embeddings": 2048,
  "model_type": "lfm2",
  "norm_eps": 1e-05,
  "num_attention_heads": 12,
  "num_hidden_layers": 18,
  "num_key_value_heads": 4,
  "pad_token_id": 0,
  "rope_parameters": {
    "rope_theta": 1000000.0,
    "rope_type": "default"
  },
  "tie_word_embeddings": true,
  "transformers_version": "5.15.0",
  "use_cache": true,
  "vocab_size": 49152
}