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

Quazim0t0 commited on 6 days ago

Commit

58e94b5

verified ·

1 Parent(s): 7476a78

Link Byrne-15M-Looped sibling and add scores note

Browse files

Files changed (1) hide show

README.md +29 -0

README.md CHANGED Viewed

@@ -39,6 +39,11 @@ It is not on the generate path.
 The base was trained on 2.0B tokens. SFT adds 0.23B, DPO adds 0.05B.
 2.3B in total. Almost all of that is the base.
 ## Checkpoints
 | file | stage | use |
@@ -1192,6 +1197,30 @@ all those modifications disabled.
 The results of the follow-up tests will be shared soon.
 ## Anti-DEGR ToolKit
 I used

 The base was trained on 2.0B tokens. SFT adds 0.23B, DPO adds 0.05B.
 2.3B in total. Almost all of that is the base.
+A smaller looped sibling lives in
+[Byrne-15M-Looped](https://huggingface.co/Quazim0t0/Byrne-15M-Looped)
+(~15.2M active, `loop_count=3`). That one is the loop A/B. This one is
+the Memory Cache / fractal RoPE run. Not a matched scale-up.
 ## Checkpoints
 | file | stage | use |
 The results of the follow-up tests will be shared soon.
+## Byrne-15M-Looped
+The small looped run is its own repo:
+[Quazim0t0/Byrne-15M-Looped](https://huggingface.co/Quazim0t0/Byrne-15M-Looped).
+~15.2M active / ~18.9M total, 10 layers, `loop_count=3` (effective
+depth 30), MoE. No Memory Cache. I trained it to see if looping the
+stack beats a param-matched `loop_count=1` baseline at equal steps.
+It does on UltraX val loss (4.195 vs 4.231 at 40k / 246M tokens) and
+on degeneration. 3× FLOPs per token. Then I SFT'd UltraChat and ran
+DPO the same way I do here.
+Scored with the same harness as this card (full MC, not 200-capped):
+| metric | 15M base | this 114M base |
+|---|---|---|
+| WikiText-2 byte_ppl ↓ | 2.943 | 2.308 |
+| BLiMP acc ↑ | 0.734 | 0.811 |
+| tokens | 0.25–0.42B | 2.0B |
+Different data, size, and knobs. The 15M numbers are in that repo
+(`BENCHMARKS.md`, `FINDINGS.md`). Safetensors for leaderboard owners
+are under `safetensors/base/` there.
 ## Anti-DEGR ToolKit
 I used