Instructions to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit 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 phanerozoic/PirateTalk-13b-v2-GGUF-16bit # Run inference directly in the terminal: llama cli -hf phanerozoic/PirateTalk-13b-v2-GGUF-16bit
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf phanerozoic/PirateTalk-13b-v2-GGUF-16bit # Run inference directly in the terminal: llama cli -hf phanerozoic/PirateTalk-13b-v2-GGUF-16bit
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 phanerozoic/PirateTalk-13b-v2-GGUF-16bit # Run inference directly in the terminal: ./llama-cli -hf phanerozoic/PirateTalk-13b-v2-GGUF-16bit
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 phanerozoic/PirateTalk-13b-v2-GGUF-16bit # Run inference directly in the terminal: ./build/bin/llama-cli -hf phanerozoic/PirateTalk-13b-v2-GGUF-16bit
Use Docker
docker model run hf.co/phanerozoic/PirateTalk-13b-v2-GGUF-16bit
- LM Studio
- Jan
- Ollama
How to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit with Ollama:
ollama run hf.co/phanerozoic/PirateTalk-13b-v2-GGUF-16bit
- Unsloth Studio
How to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit 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 phanerozoic/PirateTalk-13b-v2-GGUF-16bit 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 phanerozoic/PirateTalk-13b-v2-GGUF-16bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for phanerozoic/PirateTalk-13b-v2-GGUF-16bit to start chatting
- Docker Model Runner
How to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit with Docker Model Runner:
docker model run hf.co/phanerozoic/PirateTalk-13b-v2-GGUF-16bit
- Lemonade
How to use phanerozoic/PirateTalk-13b-v2-GGUF-16bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull phanerozoic/PirateTalk-13b-v2-GGUF-16bit
Run and chat with the model
lemonade run user.PirateTalk-13b-v2-GGUF-16bit-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
This repository presents the PirateTalk-13b-v2 model in GGUF format, offering a streamlined one-file deployment without compromising its original 16-bit performance.
Overview: PirateTalk-13b-v2 continues to reflect our dedication to exploring domain-specific dialects, marrying the precision of the Llama 2 Chat architecture with insights from the MistralPirate project.
Objective: With the adoption of the GGUF format, our emphasis is not just on the authentic portrayal of pirate speak, but also on facilitating an enhanced user experience in deploying domain-focused language models.
Base Model: Rooted in the Llama 2 13b Chat model, PirateTalk-13b-v2 leverages the power of this foundational architecture, pushing the boundaries of thematic vernacular delivery.
Dataset: The core dataset, a collection of pirate-themed entries from MistralPirate and PirateTalk-v2, remains unchanged, allowing users to dive deep into the pirate dialect with ease.
Performance Insights: PirateTalk-13b-v2 maintains its legacy of concise and linguistically rich responses, now further enhanced by the accessibility of the GGUF format.
Research Trajectories: As our journey into domain-specific dialects within language models continues, anticipate advancements in model fine-tuning, dataset evolution, and novel architectural explorations.
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