Instructions to use FinysterLin/k8s-llama-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use FinysterLin/k8s-llama-expert with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FinysterLin/k8s-llama-expert", filename="k8s-expert-rescue.Q4_K_M.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 FinysterLin/k8s-llama-expert 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 FinysterLin/k8s-llama-expert:Q4_K_M # Run inference directly in the terminal: llama cli -hf FinysterLin/k8s-llama-expert:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FinysterLin/k8s-llama-expert:Q4_K_M # Run inference directly in the terminal: llama cli -hf FinysterLin/k8s-llama-expert: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 FinysterLin/k8s-llama-expert:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FinysterLin/k8s-llama-expert: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 FinysterLin/k8s-llama-expert:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FinysterLin/k8s-llama-expert:Q4_K_M
Use Docker
docker model run hf.co/FinysterLin/k8s-llama-expert:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use FinysterLin/k8s-llama-expert with Ollama:
ollama run hf.co/FinysterLin/k8s-llama-expert:Q4_K_M
- Unsloth Studio
How to use FinysterLin/k8s-llama-expert 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 FinysterLin/k8s-llama-expert 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 FinysterLin/k8s-llama-expert to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FinysterLin/k8s-llama-expert to start chatting
- Atomic Chat new
- Docker Model Runner
How to use FinysterLin/k8s-llama-expert with Docker Model Runner:
docker model run hf.co/FinysterLin/k8s-llama-expert:Q4_K_M
- Lemonade
How to use FinysterLin/k8s-llama-expert with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FinysterLin/k8s-llama-expert:Q4_K_M
Run and chat with the model
lemonade run user.k8s-llama-expert-Q4_K_M
List all available models
lemonade list
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit library_name: transformers tags: - unsloth - llama-3 - kubernetes - devops - gguf - text-generation license: apache-2.0 language: - en
Kubernetes Expert Llama-3.1-8B (GGUF)
This model is a fine-tuned version of Llama-3.1-8B, specifically trained to be a Kubernetes Expert. It was trained using Unsloth and LoRA on a high-quality StackOverflow Kubernetes dataset.
π Model Features
- Format: GGUF (Quantized to
Q4_K_M) - Use Case: Answering technical K8s questions, debugging pods (
CrashLoopBackOff), and generating YAML configurations. - Performance: 2x faster inference speed compared to the baseline model with significantly better domain knowledge.
π¦ How to Use with Ollama
Download the Model Download
k8s-expert-rescue.Q4_K_M.gguffrom the Files tab.Create a Modelfile Create a file named
Modelfilewith the following content:FROM ./k8s-expert-rescue.Q4_K_M.gguf TEMPLATE """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: You are a Kubernetes expert. Provide a technical solution to the following problem. ### Input: {{ .Prompt }} ### Response: """ PARAMETER temperature 0.6 PARAMETER num_ctx 4096 PARAMETER stop "<|end_of_text|>" PARAMETER stop "### Instruction:" PARAMETER stop "### Input:"
- Downloads last month
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Hardware compatibility
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4-bit
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