Instructions to use bartowski/codegemma-7b-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/codegemma-7b-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/codegemma-7b-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/codegemma-7b-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bartowski/codegemma-7b-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/codegemma-7b-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/codegemma-7b-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/codegemma-7b-exl2
- SGLang
How to use bartowski/codegemma-7b-exl2 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 "bartowski/codegemma-7b-exl2" \ --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": "bartowski/codegemma-7b-exl2", "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 "bartowski/codegemma-7b-exl2" \ --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": "bartowski/codegemma-7b-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bartowski/codegemma-7b-exl2 with Docker Model Runner:
docker model run hf.co/bartowski/codegemma-7b-exl2
| library_name: transformers | |
| extra_gated_heading: Access Gemma on Hugging Face | |
| extra_gated_prompt: >- | |
| To access CodeGemma on Hugging Face, you’re required to review and agree to | |
| Google’s usage license. To do this, please ensure you’re logged-in to Hugging | |
| Face and click below. Requests are processed immediately. | |
| extra_gated_button_content: Acknowledge license | |
| license: gemma | |
| license_link: https://ai.google.dev/gemma/terms | |
| quantized_by: bartowski | |
| pipeline_tag: text-generation | |
| ## Exllama v2 Quantizations of codegemma-7b | |
| Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.18">turboderp's ExLlamaV2 v0.0.18</a> for quantization. | |
| <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> | |
| Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. | |
| Original model: https://huggingface.co/google/codegemma-7b | |
| ## Prompt format | |
| No prompt template for this model. | |
| ## Available sizes | |
| No GQA - VRAM requirements will be higher | |
| | Branch | Bits | lm_head bits | Size (4k) | Size (16k) | Description | | |
| | -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- | | |
| | [8_0](https://huggingface.co/bartowski/codegemma-7b-exl2/tree/8_0) | 8.0 | 8.0 | 14.0 GB | 19.4 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | |
| | [6_5](https://huggingface.co/bartowski/codegemma-7b-exl2/tree/6_5) | 6.5 | 8.0 | 12.5 GB | 17.9 GB | Near unquantized performance at vastly reduced size, **recommended**. | | |
| | [5_0](https://huggingface.co/bartowski/codegemma-7b-exl2/tree/5_0) | 5.0 | 6.0 | 10.9 GB | 16.3 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. | | |
| | [4_25](https://huggingface.co/bartowski/codegemma-7b-exl2/tree/4_25) | 4.25 | 6.0 | 10.2 GB | 15.7 GB | GPTQ equivalent bits per weight. | | |
| | [3_5](https://huggingface.co/bartowski/codegemma-7b-exl2/tree/3_5) | 3.5 | 6.0 | 9.5 GB | 14.9 GB | Lower quality, not recommended. | | |
| ## Download instructions | |
| With git: | |
| ```shell | |
| git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/codegemma-7b-exl2 codegemma-7b-exl2-6_5 | |
| ``` | |
| With huggingface hub (credit to TheBloke for instructions): | |
| ```shell | |
| pip3 install huggingface-hub | |
| ``` | |
| To download the `main` (only useful if you only care about measurement.json) branch to a folder called `codegemma-7b-exl2`: | |
| ```shell | |
| mkdir codegemma-7b-exl2 | |
| huggingface-cli download bartowski/codegemma-7b-exl2 --local-dir codegemma-7b-exl2 --local-dir-use-symlinks False | |
| ``` | |
| To download from a different branch, add the `--revision` parameter: | |
| Linux: | |
| ```shell | |
| mkdir codegemma-7b-exl2-6_5 | |
| huggingface-cli download bartowski/codegemma-7b-exl2 --revision 6_5 --local-dir codegemma-7b-exl2-6_5 --local-dir-use-symlinks False | |
| ``` | |
| Windows (which apparently doesn't like _ in folders sometimes?): | |
| ```shell | |
| mkdir codegemma-7b-exl2-6.5 | |
| huggingface-cli download bartowski/codegemma-7b-exl2 --revision 6_5 --local-dir codegemma-7b-exl2-6.5 --local-dir-use-symlinks False | |
| ``` | |
| Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |