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