Instructions to use kalomaze/MiniSymp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kalomaze/MiniSymp2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kalomaze/MiniSymp2",
	filename="MiniSymp2_q6_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use kalomaze/MiniSymp2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kalomaze/MiniSymp2:Q6_K
# Run inference directly in the terminal:
llama-cli -hf kalomaze/MiniSymp2:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kalomaze/MiniSymp2:Q6_K
# Run inference directly in the terminal:
llama-cli -hf kalomaze/MiniSymp2:Q6_K

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 kalomaze/MiniSymp2:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf kalomaze/MiniSymp2:Q6_K

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 kalomaze/MiniSymp2:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kalomaze/MiniSymp2:Q6_K

Use Docker

docker model run hf.co/kalomaze/MiniSymp2:Q6_K

LM Studio
Jan
Ollama
How to use kalomaze/MiniSymp2 with Ollama:
```
ollama run hf.co/kalomaze/MiniSymp2:Q6_K
```

Unsloth Studio new

How to use kalomaze/MiniSymp2 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 kalomaze/MiniSymp2 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 kalomaze/MiniSymp2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kalomaze/MiniSymp2 to start chatting

Docker Model Runner
How to use kalomaze/MiniSymp2 with Docker Model Runner:
```
docker model run hf.co/kalomaze/MiniSymp2:Q6_K
```

Lemonade

How to use kalomaze/MiniSymp2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kalomaze/MiniSymp2:Q6_K

Run and chat with the model

lemonade run user.MiniSymp2-Q6_K

List all available models

lemonade list

MiniSymp2 is A retrain of my MiniSymposium model attempt except with some more data and better practices.

added EOS tokens where they belong
made the prompt formats more diverse in the data so you could experiment / play with prompt format in context
added some new examples
measured loss curve to make sure I wasn't overfitting
used 8-bit lora instead of 4-bit qlora

Downloads last month: 24

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

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