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ArunKr
/
LLM-Models

GGUF
Model card Files Files and versions
xet
Community

Instructions to use ArunKr/LLM-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use ArunKr/LLM-Models with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="ArunKr/LLM-Models",
    	filename="Meta-Llama-3-70B_fp16_shards/-00001-of-00006.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 ArunKr/LLM-Models with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    # Run inference directly in the terminal:
    llama-cli -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    # Run inference directly in the terminal:
    llama-cli -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    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 ArunKr/LLM-Models:Q4_K_M_SHARDS
    # Run inference directly in the terminal:
    ./llama-cli -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    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 ArunKr/LLM-Models:Q4_K_M_SHARDS
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf ArunKr/LLM-Models:Q4_K_M_SHARDS
    Use Docker
    docker model run hf.co/ArunKr/LLM-Models:Q4_K_M_SHARDS
  • LM Studio
  • Jan
  • Ollama

    How to use ArunKr/LLM-Models with Ollama:

    ollama run hf.co/ArunKr/LLM-Models:Q4_K_M_SHARDS
  • Unsloth Studio new

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

    How to use ArunKr/LLM-Models with Docker Model Runner:

    docker model run hf.co/ArunKr/LLM-Models:Q4_K_M_SHARDS
  • Lemonade

    How to use ArunKr/LLM-Models with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull ArunKr/LLM-Models:Q4_K_M_SHARDS
    Run and chat with the model
    lemonade run user.LLM-Models-Q4_K_M_SHARDS
    List all available models
    lemonade list
LLM-Models
207 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 14 commits
ArunKr's picture
ArunKr
Update README.md
5eafee4 verified almost 2 years ago
  • Meta-Llama-3-70B_fp16_shards
    Upload folder using huggingface_hub about 2 years ago
  • Meta-Llama-3-8B-Instruct_fp16_Q4_K_M_shards
    Upload folder using huggingface_hub about 2 years ago
  • .gitattributes
    2.47 kB
    Upload folder using huggingface_hub about 2 years ago
  • Llama-2-7b-chat-hf_fp16.bin
    13.5 GB
    xet
    Use Git LFS to track binary files about 2 years ago
  • Meta-Llama-3-70B_fp16_Q3_K_S.bin
    30.9 GB
    xet
    Upload folder using huggingface_hub about 2 years ago
  • Meta-Llama-3-8B-Instruct_fp16.bin
    16.1 GB
    xet
    Upload folder using huggingface_hub about 2 years ago
  • README.md
    704 Bytes
    Update README.md almost 2 years ago
  • convert.sh
    425 Bytes
    Upload folder using huggingface_hub about 2 years ago
  • download.sh
    270 Bytes
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  • gguf-split
    2.71 MB
    xet
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  • main
    2.75 MB
    xet
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  • quantize
    2.72 MB
    xet
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  • quantize.sh
    103 Bytes
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  • run_gguf.sh
    281 Bytes
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  • upload.py
    183 Bytes
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