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
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Models Converted to fp16
- LLama2-chat-hf-fp16
- LLama3-7b-Instruct Model with fp16
- LLama3-70B-Instruct Model with fp16
Quantized models:
https://fossies.org/linux/llama.cpp/examples/imatrix/README.md
https://www.databricks.com/sites/default/files/2024-04/Databricks-Big-Book-Of-GenAI-FINAL.pdf
Vectordb
https://medium.com/@zilliz_learn/how-to-evaluate-a-vector-database-86dfdcc67d9b
Chunk Visualization
https://chunkviz.up.railway.app/
Prompting
https://www.promptingguide.ai/ https://learnprompting.org/docs/intro
##MLOPs https://www.databricks.com/sites/default/files/2024-06/2023-10-EB-Big-Book-of-MLOps-2nd-Edition.pdf
OpenAI Tokenizer
- Downloads last month
- 10
4-bit
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ArunKr/LLM-Models", filename="", )