Instructions to use tensorblock/defog_sqlcoder2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tensorblock/defog_sqlcoder2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/defog_sqlcoder2-GGUF", filename="sqlcoder2-Q2_K.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 tensorblock/defog_sqlcoder2-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/defog_sqlcoder2-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/defog_sqlcoder2-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/defog_sqlcoder2-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/defog_sqlcoder2-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/defog_sqlcoder2-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/defog_sqlcoder2-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/defog_sqlcoder2-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/defog_sqlcoder2-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/defog_sqlcoder2-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/defog_sqlcoder2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/defog_sqlcoder2-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/defog_sqlcoder2-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tensorblock/defog_sqlcoder2-GGUF:Q2_K
- Ollama
How to use tensorblock/defog_sqlcoder2-GGUF with Ollama:
ollama run hf.co/tensorblock/defog_sqlcoder2-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/defog_sqlcoder2-GGUF 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 tensorblock/defog_sqlcoder2-GGUF 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 tensorblock/defog_sqlcoder2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/defog_sqlcoder2-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/defog_sqlcoder2-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/defog_sqlcoder2-GGUF:Q2_K
- Lemonade
How to use tensorblock/defog_sqlcoder2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/defog_sqlcoder2-GGUF:Q2_K
Run and chat with the model
lemonade run user.defog_sqlcoder2-GGUF-Q2_K
List all available models
lemonade list
File size: 6,375 Bytes
72285de b6c5add 72285de ae63513 72285de ae63513 72285de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | ---
license: other
language:
- en
pipeline_tag: text-generation
tags:
- code
- TensorBlock
- GGUF
base_model: defog/sqlcoder2
---
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## defog/sqlcoder2 - GGUF
This repo contains GGUF format model files for [defog/sqlcoder2](https://huggingface.co/defog/sqlcoder2).
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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## Prompt template
```
Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [sqlcoder2-Q2_K.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q2_K.gguf) | Q2_K | 6.303 GB | smallest, significant quality loss - not recommended for most purposes |
| [sqlcoder2-Q3_K_S.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q3_K_S.gguf) | Q3_K_S | 7.107 GB | very small, high quality loss |
| [sqlcoder2-Q3_K_M.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q3_K_M.gguf) | Q3_K_M | 8.356 GB | very small, high quality loss |
| [sqlcoder2-Q3_K_L.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q3_K_L.gguf) | Q3_K_L | 9.262 GB | small, substantial quality loss |
| [sqlcoder2-Q4_0.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q4_0.gguf) | Q4_0 | 9.160 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [sqlcoder2-Q4_K_S.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q4_K_S.gguf) | Q4_K_S | 9.255 GB | small, greater quality loss |
| [sqlcoder2-Q4_K_M.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q4_K_M.gguf) | Q4_K_M | 10.136 GB | medium, balanced quality - recommended |
| [sqlcoder2-Q5_0.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q5_0.gguf) | Q5_0 | 11.093 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [sqlcoder2-Q5_K_S.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q5_K_S.gguf) | Q5_K_S | 11.093 GB | large, low quality loss - recommended |
| [sqlcoder2-Q5_K_M.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q5_K_M.gguf) | Q5_K_M | 11.703 GB | large, very low quality loss - recommended |
| [sqlcoder2-Q6_K.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q6_K.gguf) | Q6_K | 13.147 GB | very large, extremely low quality loss |
| [sqlcoder2-Q8_0.gguf](https://huggingface.co/tensorblock/defog_sqlcoder2-GGUF/blob/main/sqlcoder2-Q8_0.gguf) | Q8_0 | 16.966 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/defog_sqlcoder2-GGUF --include "sqlcoder2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/defog_sqlcoder2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|