Instructions to use Quazim0t0/Byrne-100M-Ultra-MC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Quazim0t0/Byrne-100M-Ultra-MC 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 Quazim0t0/Byrne-100M-Ultra-MC:F16
# Run inference directly in the terminal:
llama cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Quazim0t0/Byrne-100M-Ultra-MC:F16
# Run inference directly in the terminal:
llama cli -hf Quazim0t0/Byrne-100M-Ultra-MC: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 Quazim0t0/Byrne-100M-Ultra-MC:F16
# Run inference directly in the terminal:
./llama-cli -hf Quazim0t0/Byrne-100M-Ultra-MC: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 Quazim0t0/Byrne-100M-Ultra-MC:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quazim0t0/Byrne-100M-Ultra-MC:F16

Use Docker

docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16

LM Studio
Jan

vLLM

How to use Quazim0t0/Byrne-100M-Ultra-MC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quazim0t0/Byrne-100M-Ultra-MC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Quazim0t0/Byrne-100M-Ultra-MC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16

Ollama
How to use Quazim0t0/Byrne-100M-Ultra-MC with Ollama:
```
ollama run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
```
Unsloth Desktop
Docker Model Runner
How to use Quazim0t0/Byrne-100M-Ultra-MC with Docker Model Runner:
```
docker model run hf.co/Quazim0t0/Byrne-100M-Ultra-MC:F16
```

Lemonade

How to use Quazim0t0/Byrne-100M-Ultra-MC with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quazim0t0/Byrne-100M-Ultra-MC:F16

Run and chat with the model

lemonade run user.Byrne-100M-Ultra-MC-F16

List all available models

lemonade list

Atomic Chat

Byrne-100M-Ultra-MC / PROVENANCE.md

Quazim0t0

Add Byrne-100M-Ultra-MC inference release

ba3565a verified 12 days ago

preview code

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Byrne-100M-Ultra-MC — provenance

Byrne / SpikeWhale v2. Looped (loop_count=3), dense, 113.9M params, plus a parallel Memory Cache branch (arXiv:2602.24281).

Checkpoints

release file	came from	stage
`checkpoints/base_62k.pt`	`checkpoints_100m_ultrax_mc/step_00062000.pt`	pretrain, Dolma-blend continuation, step 62k
`checkpoints/sft_7100.pt`	`sft_if_step_00007100.pt`	SFT on HuggingFaceH4/ultrachat_200k, step 7.1k
`checkpoints/dpo_3200.pt`	`dpo_if7100_step_00003200.pt`	DPO on the SFT model, step 3.2k

Weights

Re-saved without optimizer state. Full checkpoints were ~1 GB; these are ~457 MB of model only. No NaN/Inf in any of them. max|weight| is about 18–21.

Inference path

The Memory Cache branch (and a couple of other bits) look at the whole x that forward gets. If you KV-cache the usual way, x is one token and the branch rebuilds its segments from a 1-token window. That is a different function than the one that was trained. Output still looks fluent, which is the annoying part.

This code keys MemoryCacheBranch state on cache_idx (there are 3 loop passes) so cached decode matches a full recompute token-for-token. Check with python verify.py (generate.py) or python verify_cache.py (engine). Greedy, so the ids have to match exactly. Both scripts stay; they test different stacks.

Decoding

temp 0.7, top_k 40, rep_pen 1.3. Greedy is for tests. Details in DECODING-DEFAULTS.md.

What's not here

Training scripts, Modal launchers, optimizer state, eval harnesses. Inference release only.