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

9c7f536

verified ·

1 Parent(s): 20422e9

5M 2x2 table; knock-out is not a matched train

Browse files

Files changed (1) hide show

BENCHMARKS.md +2 -2

BENCHMARKS.md CHANGED Viewed

@@ -92,8 +92,8 @@ How it may be in the CE path (not a matched-train result):
 | **knock-out, this checkpoint** | **-16.7% perplexity** |
 Gates are open; deleting the branch at inference costs ~17% PPL (was ~9%
-earlier in training). Unsure whether training without the branch would
-be worse.
 ---

 | **knock-out, this checkpoint** | **-16.7% perplexity** |
 Gates are open; deleting the branch at inference costs ~17% PPL (was ~9%
+earlier in training). That is not a matched train. At 5.7M, training
+without the branch (std) beat training with MC alone. See section 6.
 ---