Instructions to use Gargaz/brain.ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gargaz/brain.ai with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Gargaz/brain.ai",
	filename="brain.ai-phi2.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Gargaz/brain.ai with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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 Gargaz/brain.ai
# Run inference directly in the terminal:
./llama-cli -hf Gargaz/brain.ai

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 Gargaz/brain.ai
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Gargaz/brain.ai

Use Docker

docker model run hf.co/Gargaz/brain.ai

LM Studio
Jan

vLLM

How to use Gargaz/brain.ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gargaz/brain.ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gargaz/brain.ai",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gargaz/brain.ai

Ollama
How to use Gargaz/brain.ai with Ollama:
```
ollama run hf.co/Gargaz/brain.ai
```

Unsloth Studio

How to use Gargaz/brain.ai 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 Gargaz/brain.ai 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 Gargaz/brain.ai to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use Gargaz/brain.ai with Docker Model Runner:
```
docker model run hf.co/Gargaz/brain.ai
```

Lemonade

How to use Gargaz/brain.ai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Gargaz/brain.ai

Run and chat with the model

lemonade run user.brain.ai-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants