Instructions to use DataSoul/ALMA-7B-R-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DataSoul/ALMA-7B-R-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DataSoul/ALMA-7B-R-gguf", filename="ALMA-7B-R-Q3_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 DataSoul/ALMA-7B-R-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DataSoul/ALMA-7B-R-gguf:Q3_K_M # Run inference directly in the terminal: llama-cli -hf DataSoul/ALMA-7B-R-gguf:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DataSoul/ALMA-7B-R-gguf:Q3_K_M # Run inference directly in the terminal: llama-cli -hf DataSoul/ALMA-7B-R-gguf:Q3_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 DataSoul/ALMA-7B-R-gguf:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf DataSoul/ALMA-7B-R-gguf:Q3_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 DataSoul/ALMA-7B-R-gguf:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DataSoul/ALMA-7B-R-gguf:Q3_K_M
Use Docker
docker model run hf.co/DataSoul/ALMA-7B-R-gguf:Q3_K_M
- LM Studio
- Jan
- Ollama
How to use DataSoul/ALMA-7B-R-gguf with Ollama:
ollama run hf.co/DataSoul/ALMA-7B-R-gguf:Q3_K_M
- Unsloth Studio
How to use DataSoul/ALMA-7B-R-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 DataSoul/ALMA-7B-R-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 DataSoul/ALMA-7B-R-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DataSoul/ALMA-7B-R-gguf to start chatting
- Docker Model Runner
How to use DataSoul/ALMA-7B-R-gguf with Docker Model Runner:
docker model run hf.co/DataSoul/ALMA-7B-R-gguf:Q3_K_M
- Lemonade
How to use DataSoul/ALMA-7B-R-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DataSoul/ALMA-7B-R-gguf:Q3_K_M
Run and chat with the model
lemonade run user.ALMA-7B-R-gguf-Q3_K_M
List all available models
lemonade list
Upload parquet to.txt-ALMA-make imatrix.py
Browse files
parquet to.txt-ALMA-make imatrix.py
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import pandas as pd
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# first need to download from haoranxu/ALMA-R-Preference(https://huggingface.co/datasets/haoranxu/ALMA-R-Preference)
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# Parquet to txt
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df = pd.read_parquet('haoranxu-ALMA-R-Preference.parquet')
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print(df.columns)
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# text_column = df[['alma_en', 'alma_zh', 'en', 'gpt4_en', 'gpt4_zh', 'zh']]
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# text_column = df[['en', 'zh']]
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# save txt
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with open('haoranxu-ALMA-R-Preference-en-zh--zh-en.txt', 'w', encoding='utf-8') as f:
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for item in df['translation']:
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en_text = item.get('en')
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zh_text = item.get('zh')
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if en_text and zh_text: # check 'en' and 'zh'
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f.write(f"English: {en_text}\nChinese: {zh_text}\n\n")
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f.write(f"Chinese: {zh_text}\nEnglish: {en_text}\n\n")
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# then u can use it to make your language imatrix.dat
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