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

Libraries

How to use itriedcoding/Sage with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="itriedcoding/Sage",
	filename="sage-f16.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 itriedcoding/Sage 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 itriedcoding/Sage:F16
# Run inference directly in the terminal:
llama cli -hf itriedcoding/Sage:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf itriedcoding/Sage:F16
# Run inference directly in the terminal:
llama cli -hf itriedcoding/Sage: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 itriedcoding/Sage:F16
# Run inference directly in the terminal:
./llama-cli -hf itriedcoding/Sage: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 itriedcoding/Sage:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf itriedcoding/Sage:F16

Use Docker

docker model run hf.co/itriedcoding/Sage:F16

LM Studio
Jan
Ollama
How to use itriedcoding/Sage with Ollama:
```
ollama run hf.co/itriedcoding/Sage:F16
```

Unsloth Studio

How to use itriedcoding/Sage 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 itriedcoding/Sage 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 itriedcoding/Sage to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use itriedcoding/Sage with Docker Model Runner:
```
docker model run hf.co/itriedcoding/Sage:F16
```

Lemonade

How to use itriedcoding/Sage with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull itriedcoding/Sage:F16

Run and chat with the model

lemonade run user.Sage-F16

List all available models

lemonade list

Sage

File size: 346 Bytes

64728f0

{
  "architectures": [
    "TransformerLM"
  ],
  "model_type": "transformer_lm",
  "vocab_size": 40,
  "hidden_size": 256,
  "num_hidden_layers": 4,
  "num_attention_heads": 8,
  "intermediate_size": 1024,
  "max_position_embeddings": 64,
  "pad_token_id": 0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "torch_dtype": "float32"
}