Instructions to use tensorblock/codegemma-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/codegemma-7b-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensorblock/codegemma-7b-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/codegemma-7b-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/codegemma-7b-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/codegemma-7b-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/codegemma-7b-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/codegemma-7b-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/codegemma-7b-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/codegemma-7b-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/codegemma-7b-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/codegemma-7b-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/codegemma-7b-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/codegemma-7b-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/codegemma-7b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/codegemma-7b-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/codegemma-7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tensorblock/codegemma-7b-GGUF:Q2_K
- SGLang
How to use tensorblock/codegemma-7b-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tensorblock/codegemma-7b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/codegemma-7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tensorblock/codegemma-7b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/codegemma-7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use tensorblock/codegemma-7b-GGUF with Ollama:
ollama run hf.co/tensorblock/codegemma-7b-GGUF:Q2_K
- Unsloth Studio
How to use tensorblock/codegemma-7b-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/codegemma-7b-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/codegemma-7b-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/codegemma-7b-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/codegemma-7b-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/codegemma-7b-GGUF:Q2_K
- Lemonade
How to use tensorblock/codegemma-7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/codegemma-7b-GGUF:Q2_K
Run and chat with the model
lemonade run user.codegemma-7b-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
File size: 6,413 Bytes
4f4a3b3 ec4cdcc 4f4a3b3 ec4cdcc 4f4a3b3 2b5f7b9 4f4a3b3 ec4cdcc 4f4a3b3 ec4cdcc 4f4a3b3 ec4cdcc 4f4a3b3 10d25a1 5687cdf 10d25a1 5687cdf 10d25a1 4f4a3b3 ec4cdcc 4f4a3b3 ec4cdcc 4f4a3b3 ec4cdcc 4f4a3b3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | ---
tags:
- code
- gemma
- TensorBlock
- GGUF
library_name: transformers
pipeline_tag: text-generation
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: TechxGenus/CodeGemma-7b
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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## TechxGenus/CodeGemma-7b - GGUF
This repo contains GGUF format model files for [TechxGenus/CodeGemma-7b](https://huggingface.co/TechxGenus/CodeGemma-7b).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π Try it now! π</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
</tr>
</table>
## Prompt template
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [CodeGemma-7b-Q2_K.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q2_K.gguf) | Q2_K | 3.481 GB | smallest, significant quality loss - not recommended for most purposes |
| [CodeGemma-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_S.gguf) | Q3_K_S | 3.982 GB | very small, high quality loss |
| [CodeGemma-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_M.gguf) | Q3_K_M | 4.369 GB | very small, high quality loss |
| [CodeGemma-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q3_K_L.gguf) | Q3_K_L | 4.709 GB | small, substantial quality loss |
| [CodeGemma-7b-Q4_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_0.gguf) | Q4_0 | 5.012 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [CodeGemma-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_K_S.gguf) | Q4_K_S | 5.046 GB | small, greater quality loss |
| [CodeGemma-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q4_K_M.gguf) | Q4_K_M | 5.330 GB | medium, balanced quality - recommended |
| [CodeGemma-7b-Q5_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_0.gguf) | Q5_0 | 5.981 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [CodeGemma-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_K_S.gguf) | Q5_K_S | 5.981 GB | large, low quality loss - recommended |
| [CodeGemma-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q5_K_M.gguf) | Q5_K_M | 6.145 GB | large, very low quality loss - recommended |
| [CodeGemma-7b-Q6_K.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q6_K.gguf) | Q6_K | 7.010 GB | very large, extremely low quality loss |
| [CodeGemma-7b-Q8_0.gguf](https://huggingface.co/tensorblock/CodeGemma-7b-GGUF/blob/main/CodeGemma-7b-Q8_0.gguf) | Q8_0 | 9.078 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/CodeGemma-7b-GGUF --include "CodeGemma-7b-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/CodeGemma-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|