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

Unseen1980 commited on 10 days ago

Commit

6aa2c94

verified ·

1 Parent(s): b18e837

pointer: latest-rolling-abl-arch-dense-150m.json

Browse files

Files changed (1) hide show

latest-rolling-abl-arch-dense-150m.json +3 -3

latest-rolling-abl-arch-dense-150m.json CHANGED Viewed

@@ -1,9 +1,9 @@
 {
   "revision": "rolling",
   "path_in_repo": "rolling/abl-arch-dense-150m/weights.pt",
-  "step": 9016,
-  "tokens_seen": 4279187456,
   "kind": "rolling",
   "run_name": "abl-arch-dense-150m",
-  "updated_at": 1786422921.9830732
 }

 {
   "revision": "rolling",
   "path_in_repo": "rolling/abl-arch-dense-150m/weights.pt",
+  "step": 10391,
+  "tokens_seen": 5000083456,
   "kind": "rolling",
   "run_name": "abl-arch-dense-150m",
+  "updated_at": 1786429644.6507075
 }