Instructions to use TobDeBer/myContainers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use TobDeBer/myContainers with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TobDeBer/myContainers", filename="arco_BE8.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 TobDeBer/myContainers with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TobDeBer/myContainers # Run inference directly in the terminal: llama-cli -hf TobDeBer/myContainers
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TobDeBer/myContainers # Run inference directly in the terminal: llama-cli -hf TobDeBer/myContainers
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 TobDeBer/myContainers # Run inference directly in the terminal: ./llama-cli -hf TobDeBer/myContainers
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 TobDeBer/myContainers # Run inference directly in the terminal: ./build/bin/llama-cli -hf TobDeBer/myContainers
Use Docker
docker model run hf.co/TobDeBer/myContainers
- LM Studio
- Jan
- Ollama
How to use TobDeBer/myContainers with Ollama:
ollama run hf.co/TobDeBer/myContainers
- Unsloth Studio new
How to use TobDeBer/myContainers 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 TobDeBer/myContainers 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 TobDeBer/myContainers to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TobDeBer/myContainers to start chatting
- Docker Model Runner
How to use TobDeBer/myContainers with Docker Model Runner:
docker model run hf.co/TobDeBer/myContainers
- Lemonade
How to use TobDeBer/myContainers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TobDeBer/myContainers
Run and chat with the model
lemonade run user.myContainers-{{QUANT_TAG}}List all available models
lemonade list
assistant patches
Browse files- assistant.patch +50 -0
assistant.patch
ADDED
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diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html
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index c87dd8f1..d1aed025 100644
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--- a/examples/server/public/index-new.html
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+++ b/examples/server/public/index-new.html
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@@ -1063,7 +1063,7 @@ return html`
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return html`
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<div class="mode-${session.value.type}">
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<header>
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- <h2>llama.cpp</h2>
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+ <h2>modelname</h2>
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<div class="dropdown">
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<button class="dropbtn"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><circle cx="12" cy="12" r="10" stroke-width="2"/></svg></button>
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<div class="dropdown-content" id="theme-selector">
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diff --git a/examples/server/public/index.html b/examples/server/public/index.html
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index 07fec6a3..0d873f3f 100644
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--- a/examples/server/public/index.html
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+++ b/examples/server/public/index.html
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@@ -3,7 +3,7 @@
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
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<meta name="color-scheme" content="light dark">
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- <title>llama.cpp - chat</title>
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+ <title>assistantname - chat</title>
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<style>
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body {
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var slot_id = -1;
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const session = signal({
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- prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
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+ prompt: "This is a conversation between User and assistantname, a friendly chatbot. assistantname is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
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template: "{{prompt}}\n\n{{history}}\n{{char}}:",
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historyTemplate: "{{name}}: {{message}}",
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transcript: [],
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type: "chat", // "chat" | "completion"
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- char: "Llama",
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+ char: "assistantname",
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user: "User",
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image_selected: ''
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})
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<header>
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<div class="grid-container">
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<div class="grid-item"></div>
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- <div class="grid-item"><h1>llama.cpp</h1></div>
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+ <div class="grid-item"><h1>modelname</h1></div>
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<div class="grid-item"><a class="customlink" href="index-new.html">New UI</a></div>
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</div>
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</header>
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