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Instructions to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF 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 tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-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 tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
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 tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
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 tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
- Ollama
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with Ollama:
ollama run hf.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
- Unsloth Desktop
- Pi
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
- Lemonade
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Run and chat with the model
lemonade run user.markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF:Q2_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
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---
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license: llama3
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base_model: markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0
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language:
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| 5 |
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- en
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- zh
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- es
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pipeline_tag: text-generation
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tags:
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- code
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- spatial
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- sql
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- GIS
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- PostGIS
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- TensorBlock
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| 16 |
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- GGUF
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| 17 |
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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| 25 |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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</p>
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</div>
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</div>
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## markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0 - GGUF
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This repo contains GGUF format model files for [markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0](https://huggingface.co/markrodrigo/Llama-3.1-8B-Instruct-Spatial-SQL-1.0).
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).
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## Our projects
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<table border="1" cellspacing="0" cellpadding="10">
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<tr>
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<th style="font-size: 25px;">Awesome MCP Servers</th>
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<th style="font-size: 25px;">TensorBlock Studio</th>
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</tr>
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<tr>
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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
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<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
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</tr>
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<tr>
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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</tr>
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<tr>
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<th>
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<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">๐ See what we built ๐</a>
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</th>
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<th>
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<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">๐ See what we built ๐</a>
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</th>
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</tr>
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</table>
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## Prompt template
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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Cutting Knowledge Date: December 2023
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Today Date: 26 Jul 2024
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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## Model file specification
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q2_K.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_0.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_0.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q6_K.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
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| [Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q8_0.gguf](https://huggingface.co/tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF/blob/main/Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q8_0.gguf) | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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### Command line
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Firstly, install Huggingface Client
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```shell
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
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```shell
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huggingface-cli download tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF --include "Llama-3.1-8B-Instruct-Spatial-SQL-1.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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```
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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```shell
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huggingface-cli download tensorblock/markrodrigo_Llama-3.1-8B-Instruct-Spatial-SQL-1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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