Instructions to use cstech-ftw/mystreamerproject 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 cstech-ftw/mystreamerproject 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 cstech-ftw/mystreamerproject:Q4_K_M # Run inference directly in the terminal: llama cli -hf cstech-ftw/mystreamerproject:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cstech-ftw/mystreamerproject:Q4_K_M # Run inference directly in the terminal: llama cli -hf cstech-ftw/mystreamerproject:Q4_K_M
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 cstech-ftw/mystreamerproject:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cstech-ftw/mystreamerproject:Q4_K_M
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 cstech-ftw/mystreamerproject:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cstech-ftw/mystreamerproject:Q4_K_M
Use Docker
docker model run hf.co/cstech-ftw/mystreamerproject:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use cstech-ftw/mystreamerproject with Ollama:
ollama run hf.co/cstech-ftw/mystreamerproject:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cstech-ftw/mystreamerproject with Docker Model Runner:
docker model run hf.co/cstech-ftw/mystreamerproject:Q4_K_M
- Lemonade
How to use cstech-ftw/mystreamerproject with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cstech-ftw/mystreamerproject:Q4_K_M
Run and chat with the model
lemonade run user.mystreamerproject-Q4_K_M
List all available models
lemonade list
- Atomic Chat
MyStreamer AI Model Quants
This repository provides quantized GGUF weights optimized for use with the MyStreamer client.
Models Included
1. Lightweight Tier: Llama-3.2-3B-Instruct-Abliterated
- Base Architecture: Meta Llama 3.2 3B Instruct
- Upstream Fine-tune: huihui-ai/Llama-3.2-3B-Instruct-abliterated
- Target Hardware: 6 GB VRAM GPUs (GTX 1660 Super, RTX 2060, RTX 3050)
2. High Quality Tier: L3-8B-Stheno-v3.2
- Base Architecture: Meta Llama 3 8B Instruct
- Upstream Creator / Fine-Tuner: Sao10K/L3-8B-Stheno-v3.2
- Target Hardware: 8 GB โ 12 GB+ VRAM GPUs (RTX 3060, RTX 4060, RTX 3070+)
License & Attribution
- Built with Meta Llama 3 and Meta Llama 3.2.
- Distributed under the terms of the Meta Llama 3 / 3.2 Community License Agreement.
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