Instructions to use Sosnitskij/polylm-multialpaca-13b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Sosnitskij/polylm-multialpaca-13b-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Sosnitskij/polylm-multialpaca-13b-gguf", filename="polylm-multialpaca-13b-Q5_K_M.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 Sosnitskij/polylm-multialpaca-13b-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M # Run inference directly in the terminal: llama-cli -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M # Run inference directly in the terminal: llama-cli -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_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 Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_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 Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
Use Docker
docker model run hf.co/Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use Sosnitskij/polylm-multialpaca-13b-gguf with Ollama:
ollama run hf.co/Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
- Unsloth Studio new
How to use Sosnitskij/polylm-multialpaca-13b-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 Sosnitskij/polylm-multialpaca-13b-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 Sosnitskij/polylm-multialpaca-13b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sosnitskij/polylm-multialpaca-13b-gguf to start chatting
- Docker Model Runner
How to use Sosnitskij/polylm-multialpaca-13b-gguf with Docker Model Runner:
docker model run hf.co/Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
- Lemonade
How to use Sosnitskij/polylm-multialpaca-13b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sosnitskij/polylm-multialpaca-13b-gguf:Q5_K_M
Run and chat with the model
lemonade run user.polylm-multialpaca-13b-gguf-Q5_K_M
List all available models
lemonade list
Original model: https://huggingface.co/DAMO-NLP-MT/polylm-multialpaca-13b
Model Card for PolyLM-Multialpaca
This model is finetuned on polyLM-13b using multialpaca (a self-instruction dataset)
Demo
Bias, Risks, and Limitations
The information below in this section are copied from the model's official model card:
Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
This version activates the instruction-following capability of PolyLM through self-instruction, but currently, the training instructions are relatively simple and the support for abilities such as multi-turn dialogue, context understanding, CoT, Plugin, etc. is not very friendly. We are making efforts to develop a new version.
Citation
BibTeX:
@misc{wei2023polylm,
title={PolyLM: An Open Source Polyglot Large Language Model},
author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie},
year={2023},
eprint={2307.06018},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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