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

786f87d

verified ·

1 Parent(s): 58e94b5

15M sibling is the toy/smoke-test for this run

Browse files

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -41,8 +41,9 @@ The base was trained on 2.0B tokens. SFT adds 0.23B, DPO adds 0.05B.
 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
@@ -1201,6 +1202,7 @@ The results of the follow-up tests will be shared soon.
 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

 A smaller looped sibling lives in
 [Byrne-15M-Looped](https://huggingface.co/Quazim0t0/Byrne-15M-Looped)
+(~15.2M active, `loop_count=3`). That was the toy / smoke-test for
+this run: looping at 15M before I spent the 114M budget. Not a
+matched scale-up.
 ## Checkpoints
 The small looped run is its own repo:
 [Quazim0t0/Byrne-15M-Looped](https://huggingface.co/Quazim0t0/Byrne-15M-Looped).
+That was the toy / smoke-test for this 114M model.
 ~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