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oytunistrator
/
AquaLLM

Question Answering
Safetensors
GGUF
Turkish
English
gpt2
aquarium
aqua
biology
text-generation-inference
Model card Files Files and versions
xet

Instructions to use oytunistrator/AquaLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use oytunistrator/AquaLLM with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="oytunistrator/AquaLLM",
    	filename="aquallm.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 oytunistrator/AquaLLM with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf oytunistrator/AquaLLM
    # Run inference directly in the terminal:
    llama-cli -hf oytunistrator/AquaLLM
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf oytunistrator/AquaLLM
    # Run inference directly in the terminal:
    llama-cli -hf oytunistrator/AquaLLM
    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 oytunistrator/AquaLLM
    # Run inference directly in the terminal:
    ./llama-cli -hf oytunistrator/AquaLLM
    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 oytunistrator/AquaLLM
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf oytunistrator/AquaLLM
    Use Docker
    docker model run hf.co/oytunistrator/AquaLLM
  • LM Studio
  • Jan
  • Ollama

    How to use oytunistrator/AquaLLM with Ollama:

    ollama run hf.co/oytunistrator/AquaLLM
  • Unsloth Studio new

    How to use oytunistrator/AquaLLM 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 oytunistrator/AquaLLM 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 oytunistrator/AquaLLM to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for oytunistrator/AquaLLM to start chatting
  • Docker Model Runner

    How to use oytunistrator/AquaLLM with Docker Model Runner:

    docker model run hf.co/oytunistrator/AquaLLM
  • Lemonade

    How to use oytunistrator/AquaLLM with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull oytunistrator/AquaLLM
    Run and chat with the model
    lemonade run user.AquaLLM-{{QUANT_TAG}}
    List all available models
    lemonade list
AquaLLM
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  • 1 contributor
History: 10 commits
oytunistrator's picture
oytunistrator
Upload aquallm.gguf with huggingface_hub
e944de7 verified 8 months ago
  • checkpoint-39
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  • .gitattributes
    1.68 kB
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  • LICENSE
    2.89 kB
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  • README.md
    1.55 kB
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  • added_tokens.json
    735 Bytes
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  • aquallm.gguf
    252 MB
    xet
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  • chat_template.jinja
    4.2 kB
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  • config.json
    880 Bytes
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  • generation_config.json
    119 Bytes
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  • merges.txt
    456 kB
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  • model.safetensors
    498 MB
    xet
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  • special_tokens_map.json
    583 Bytes
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  • tokenizer.json
    3.56 MB
    xet
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  • tokenizer_config.json
    698 Bytes
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  • training_args.bin
    5.65 kB
    xet
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  • vocab.json
    798 kB
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  • vocab.txt
    996 kB
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