Instructions to use ReySajju742/Qalb-1.0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ReySajju742/Qalb-1.0-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ReySajju742/Qalb-1.0-gguf", filename="Qalb-1.0-8B-Instruct-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ReySajju742/Qalb-1.0-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ReySajju742/Qalb-1.0-gguf:F16 # Run inference directly in the terminal: llama cli -hf ReySajju742/Qalb-1.0-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ReySajju742/Qalb-1.0-gguf:F16 # Run inference directly in the terminal: llama cli -hf ReySajju742/Qalb-1.0-gguf:F16
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 ReySajju742/Qalb-1.0-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf ReySajju742/Qalb-1.0-gguf:F16
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 ReySajju742/Qalb-1.0-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ReySajju742/Qalb-1.0-gguf:F16
Use Docker
docker model run hf.co/ReySajju742/Qalb-1.0-gguf:F16
- LM Studio
- Jan
- Ollama
How to use ReySajju742/Qalb-1.0-gguf with Ollama:
ollama run hf.co/ReySajju742/Qalb-1.0-gguf:F16
- Unsloth Studio
How to use ReySajju742/Qalb-1.0-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 ReySajju742/Qalb-1.0-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 ReySajju742/Qalb-1.0-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ReySajju742/Qalb-1.0-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ReySajju742/Qalb-1.0-gguf with Docker Model Runner:
docker model run hf.co/ReySajju742/Qalb-1.0-gguf:F16
- Lemonade
How to use ReySajju742/Qalb-1.0-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ReySajju742/Qalb-1.0-gguf:F16
Run and chat with the model
lemonade run user.Qalb-1.0-gguf-F16
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ReySajju742/Qalb-1.0-gguf:F16# Run inference directly in the terminal:
llama cli -hf ReySajju742/Qalb-1.0-gguf:F16Use 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 ReySajju742/Qalb-1.0-gguf:F16# Run inference directly in the terminal:
./llama-cli -hf ReySajju742/Qalb-1.0-gguf:F16Build 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 ReySajju742/Qalb-1.0-gguf:F16# Run inference directly in the terminal:
./build/bin/llama-cli -hf ReySajju742/Qalb-1.0-gguf:F16Use Docker
docker model run hf.co/ReySajju742/Qalb-1.0-gguf:F16Qalb-1.0-8B-Instruct - GGUF
This repository contains the GGUF (GGML Unified Format) version of the enstazao/Qalb-1.0-8B-Instruct model.
Origin
This model was converted from its original Hugging Face format using the llama.cpp project.
Purpose
Qalb-1.0-8B-Instruct is a general-purpose large language model, suitable for various natural language processing tasks, including text generation, question answering, and summarization.
How to Use with Ollama
To use this GGUF model with Ollama, you can run the following command:
ollama run hf.co/ReySajju742/Qalb-1.0-gguf
This command will automatically download and set up the model for use with Ollama.
Potential Enhancements
- Quantization: This model is currently available in
f16format. For reduced size and potentially faster inference with minimal performance impact, consider quantizing it further (e.g., toQ4_K_M). This can be done using thellama.cpptools. - LoRA Adapters: The
llama.cppecosystem also supports integrating LoRA (Low-Rank Adaptation) adapters, which can fine-tune the model for specific tasks or datasets without requiring a full model conversion.
For more details on these enhancements, please refer to the llama.cpp GitHub repository.
Reproduction Instructions
The GGUF conversion process for this model involved the following steps:
- Downloading the Hugging Face Model: The original
enstazao/Qalb-1.0-8B-Instructmodel was downloaded from Hugging Face. - Cloning
llama.cpp: Thellama.cpprepository was cloned to access its conversion tools. - Converting to GGUF: The downloaded Hugging Face model was converted to the GGUF format (f16) using the
llama.cpp'sconvert_hf_to_gguf.pyscript. - Uploading to Hugging Face: The resulting GGUF file was then uploaded to this repository (
ReySajju742/Qalb-1.0-gguf).
This entire process can be reproduced using the provided Colab notebook, which automates these steps.
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Model tree for ReySajju742/Qalb-1.0-gguf
Base model
unsloth/Meta-Llama-3.1-8B
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf ReySajju742/Qalb-1.0-gguf:F16# Run inference directly in the terminal: llama cli -hf ReySajju742/Qalb-1.0-gguf:F16