Instructions to use alpaca-core/ac-test-data-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpaca-core/ac-test-data-llama with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alpaca-core/ac-test-data-llama",
	filename="bge-small-en-v1.5-f16.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 alpaca-core/ac-test-data-llama with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alpaca-core/ac-test-data-llama:F16
# Run inference directly in the terminal:
llama-cli -hf alpaca-core/ac-test-data-llama:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alpaca-core/ac-test-data-llama:F16
# Run inference directly in the terminal:
llama-cli -hf alpaca-core/ac-test-data-llama: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 alpaca-core/ac-test-data-llama:F16
# Run inference directly in the terminal:
./llama-cli -hf alpaca-core/ac-test-data-llama: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 alpaca-core/ac-test-data-llama:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alpaca-core/ac-test-data-llama:F16

Use Docker

docker model run hf.co/alpaca-core/ac-test-data-llama:F16

LM Studio
Jan
Ollama
How to use alpaca-core/ac-test-data-llama with Ollama:
```
ollama run hf.co/alpaca-core/ac-test-data-llama:F16
```

Unsloth Studio new

How to use alpaca-core/ac-test-data-llama 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 alpaca-core/ac-test-data-llama 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 alpaca-core/ac-test-data-llama to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alpaca-core/ac-test-data-llama to start chatting

Docker Model Runner
How to use alpaca-core/ac-test-data-llama with Docker Model Runner:
```
docker model run hf.co/alpaca-core/ac-test-data-llama:F16
```

Lemonade

How to use alpaca-core/ac-test-data-llama with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alpaca-core/ac-test-data-llama:F16

Run and chat with the model

lemonade run user.ac-test-data-llama-F16

List all available models

lemonade list

Alpaca Core Test Data for `llama.cpp` Inference

Weights Files

gpt2-117m-f16 - from https://huggingface.co/aisuko/gpt2-117M-gguf
gpt2-117m-q6_k - from https://huggingface.co/RichardErkhov/openai-community_-_gpt2-gguf
bge-small-en-v1.5-f16.gguf - from https://huggingface.co/CompendiumLabs/bge-small-en-v1.5-gguf

Downloads last month: 13

GGUF

Model size

33.2M params

Architecture

bert

Hardware compatibility

6-bit

16-bit

View +1 variant

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Alpaca Core Test Data for llama.cpp Inference

Weights Files

Alpaca Core Test Data for `llama.cpp` Inference