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
Upload folder using huggingface_hub
Browse files- Meta-Llama-3-70B_fp16_Q3_K_S.bin +3 -0
- run_gguf.sh +3 -1
Meta-Llama-3-70B_fp16_Q3_K_S.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:768c31b519abda73053385e10077da8a2abb1f34a8c1349e29f6754f4ba35d2f
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size 30912050240
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run_gguf.sh
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./main -m output/Meta-Llama-3-
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#./main -m output/Meta-Llama-3-8B-Instruct_fp16.bin -p "hi"
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#./main -m output/converted_f16.bin -p "hi"
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./main -m output/Meta-Llama-3-70B_fp16_Q3_K_S.bin -p "hi"
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#./main -m output/Meta-Llama-3-8B-Instruct_fp16_Q4_K_M.bin -p "hi"
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#./main -m output/Meta-Llama-3-70B_fp16.bin -p "hi"
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#./main -m output/Meta-Llama-3-8B-Instruct_fp16.bin -p "hi"
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#./main -m output/converted_f16.bin -p "hi"
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