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 3 days ago

Commit

5e9572f

verified ·

1 Parent(s): ceb9938

instruct README.md

Browse files

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hf/instruct/README.md +72 -0

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- daedalus
+- cpu-inference
+- gguf
+- q4_0
+---
+# daedalus-150m
+A 160.5M-parameter causal LM built for the best quality-per-token-per-second on **CPU** inference, exported to GGUF Q4_0 for llama.cpp.
+## What this model is trying to beat
+> Beat Pythia-160M, OPT-125M and GPT-neo-125M on quality; target MobileLLM-125M as a stretch; concede SmolLM2-135M on quality while beating it decisively on CPU decode.
+This bar was fixed before any result landed. Numbers below are reported against it whether or not they clear it.
+## Architecture
+| | |
+|---|---|
+| exported as | `Lfm2ForCausalLM` |
+| parameters | 160,488,960 (122,740,224 non-embedding) |
+| blocks | 18 (`ccccAccAcAcAcAccAc` -- `c` = gated short conv, `A` = GQA attention) |
+| hidden size | 768 |
+| SwiGLU inner dim | 2048 |
+| heads | 12 query / 4 KV, head_dim 64, QK-norm |
+| RoPE theta | 1,000,000 |
+| context | 2048 |
+| tied embeddings | True |
+| tokenizer | [`HuggingFaceTB/SmolLM2-135M`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M), reused byte-identical, vocab 49,152 |
+## Training
+- run: `post-sft`
+- Muon lr 0.002 on 2D hidden matrices; AdamW lr 3e-05 on embeddings/head/norms
+- WSD schedule, linear decay to zero over the final 45% of the run
+## Evaluation
+_Not yet measured for this export._
+## Q4_0 quantization
+- fp16 perplexity **217.8803** vs Q4_0 **263.4498**
+- delta **20.915%**
+- passes the <1.0% threshold: **False**
+- llama.cpp CPU decode at 64 threads, by context depth:
+  - depth **0**: **118.0 tok/s** (+/- 3.8)
+  - depth **512**: **103.3 tok/s** (+/- 3.4)
+  - depth **2048** (the trained context): **97.5 tok/s** (+/- 2.4)
+## Checkpoints and how to continue training
+Checkpoints are pushed to the private Hub model repo **`Unseen1980/daedalus-checkpoints`**: weights-only bf16 rolling copies every ~2 h under `rolling/<run>/weights.pt`, plus a milestone with full Muon + AdamW optimizer state at the WSD decay-start step on its own revision.
+_No milestone record was found beside this checkpoint, so no branch point is published for it._
+## Deviations from the blueprint
+Each was costed and approved rather than silently dropped; see `DAEDALUS-BLUEPRINT-v6.md` and issue #4.
+- **No distillation** from SmolLM2-1.7B during decay. 288 GB of top-16 logits does not fit the disk and the online-teacher variant cost ~$29 of a $94.66 budget; its own evidence was only "+1-3 points plausible".
+- **Corpus stops at ~14.2B tokens, not 45B.** Training repeats a balanced corpus rather than seeing 45B unique tokens; at this scale repetition up to ~4 epochs costs little against fresh tokens, and mixture balance mattered more than raw size.
+- **Document-aligned packing not implemented** -- sequences may cross document boundaries.
+- **NoPE skipped** -- it breaks GGUF export.
+- **Single seed** for the hero run, so no seed-sigma is reported.
+- **`everyday-conversations` contributes ~0.00%** of pretraining instead of its 2% share (the whole dataset is 0.4M tokens, which the 4-epoch cap reduces to nothing); dialogue enters at the `post` SFT stage instead.