Instructions to use QuantFactory/llama-160m-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/llama-160m-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/llama-160m-GGUF",
	filename="llama-160m.Q2_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 QuantFactory/llama-160m-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-160m-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-160m-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-160m-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-160m-GGUF:Q4_K_M

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 QuantFactory/llama-160m-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/llama-160m-GGUF:Q4_K_M

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 QuantFactory/llama-160m-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/llama-160m-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/llama-160m-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/llama-160m-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/llama-160m-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/llama-160m-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/llama-160m-GGUF:Q4_K_M

Ollama
How to use QuantFactory/llama-160m-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/llama-160m-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/llama-160m-GGUF 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 QuantFactory/llama-160m-GGUF 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 QuantFactory/llama-160m-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/llama-160m-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/llama-160m-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/llama-160m-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/llama-160m-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/llama-160m-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-160m-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/llama-160m-GGUF

This is quantized version of JackFram/llama-160m created using llama.cpp

Original Model Card

Model description

This is a LLaMA-like model with only 160M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.

No evaluation has been conducted yet, so use it with care.

The model is mainly developed as a base Small Speculative Model in the SpecInfer paper.

Citation

To cite the model, please use

@misc{miao2023specinfer,
      title={SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification}, 
      author={Xupeng Miao and Gabriele Oliaro and Zhihao Zhang and Xinhao Cheng and Zeyu Wang and Rae Ying Yee Wong and Zhuoming Chen and Daiyaan Arfeen and Reyna Abhyankar and Zhihao Jia},
      year={2023},
      eprint={2305.09781},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 95

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Dataset used to train QuantFactory/llama-160m-GGUF

Paper for QuantFactory/llama-160m-GGUF

SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification

Paper • 2305.09781 • Published May 16, 2023 • 4