Instructions to use mitmul/plamo-2-translate-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitmul/plamo-2-translate-GGUF with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="mitmul/plamo-2-translate-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mitmul/plamo-2-translate-GGUF", dtype="auto") - llama-cpp-python
How to use mitmul/plamo-2-translate-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mitmul/plamo-2-translate-GGUF", filename="plamo-2-translate-IQ4_XS.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 mitmul/plamo-2-translate-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mitmul/plamo-2-translate-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mitmul/plamo-2-translate-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 mitmul/plamo-2-translate-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mitmul/plamo-2-translate-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 mitmul/plamo-2-translate-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mitmul/plamo-2-translate-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 mitmul/plamo-2-translate-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mitmul/plamo-2-translate-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mitmul/plamo-2-translate-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mitmul/plamo-2-translate-GGUF with Ollama:
ollama run hf.co/mitmul/plamo-2-translate-GGUF:Q4_K_M
- Unsloth Studio new
How to use mitmul/plamo-2-translate-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 mitmul/plamo-2-translate-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 mitmul/plamo-2-translate-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mitmul/plamo-2-translate-GGUF to start chatting
- Docker Model Runner
How to use mitmul/plamo-2-translate-GGUF with Docker Model Runner:
docker model run hf.co/mitmul/plamo-2-translate-GGUF:Q4_K_M
- Lemonade
How to use mitmul/plamo-2-translate-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mitmul/plamo-2-translate-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.plamo-2-translate-GGUF-Q4_K_M
List all available models
lemonade list
PLaMo Translation Model
PLaMo翻訳モデルはPreferred Networksによって開発された翻訳向け特化型大規模言語モデルです。 詳しくはブログ記事およびプレスリリースを参照してください。
PLaMo Translation Model is a specialized large-scale language model developed by Preferred Networks for translation tasks. For details, please refer to the blog post and press release.
This repository contains some GGUF converted models of PLaMo-2-Translate. There are three models available:
mitmul/plamo-2-translate-IQ4_XS.gguf: A GGUF converted model ofpfnet/plamo-2-translatewith IQ4 quantization.mitmul/plamo-2-translate-Q5_K_M.gguf: A GGUF converted model ofpfnet/plamo-2-translatewith Q5_K_M quantization.mitmul/plamo-2-translate-.gguf:
PLaMo Translation Model is released under PLaMo community license. Please check the following license and agree to this before downloading.
- (EN) under construction: we apologize for the inconvenience
- (JA) https://www.preferred.jp/ja/plamo-community-license/
NOTE: This model has NOT been instruction-tuned for chat dialog or other downstream tasks.
For commercial users
Please check the PLaMo community license and contact us via the following form to use commercial purpose.
Usage
Build llama.cpp
git clone -b mitmul/add-plamo2 https://github.com/mitmul/llama.cpp
cd llama.cpp
cmake -B release
cmake --build release --config Release -j
(If you want to build llama.cpp with CUDA-support etc., please specify the required options based on the llama.cpp README.)
Download the model
git clone https://huggingface.co/mitmul/plamo-2-translate-GGUF
Run the model
$ ./release/bin/llama-cli \
-m plamo-2-translate-IQ4_XS.gguf \
--jinja --chat-template-file chat_template.jinja2 \
-p "こんにちは" -sp --verbose-prompt
(...verbose outputs...)
<|plamo:op|>dataset
translation
<|plamo:op|>input lang=English|Japanese
こんにゃくは太らない!
<|plamo:op|>output
Konjac won't make you gain weight!
<|plamo:op|>
> あのイーハトーヴォのすきとおった風、夏でも底に冷たさをもつ青いそら、うつくしい森で飾られたモリーオ市、郊外のぎらぎらひかる草の波。
That clear wind from Ihatovo, the blue sky that retains its coolness even in summer, the city of Morio city, beautifully adorned with forests, and the glittering waves of grass in the suburbs.
<|plamo:op|>
Bias, Risks, and Limitations
PLaMo Translation Model is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo Translation Model’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo Translation Model, developers should perform safety testing and tuning tailored to their specific applications of the model.
Acknowledgement
This model is trained under the project, “Research and Development Project of the Enhanced Infrastructures for Post 5G Information and Communication System” (JPNP 20017), subsidized by the New Energy and Industrial Technology Development Organization (NEDO).
AI policies for Preferred Networks, Inc. group
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Model tree for mitmul/plamo-2-translate-GGUF
Base model
pfnet/plamo-2-8b