Instructions to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="yeoj34760/translategemma-27b-it-Q4_K_M-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yeoj34760/translategemma-27b-it-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yeoj34760/translategemma-27b-it-Q4_K_M-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 yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf yeoj34760/translategemma-27b-it-Q4_K_M-GGUF: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 yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf yeoj34760/translategemma-27b-it-Q4_K_M-GGUF: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 yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yeoj34760/translategemma-27b-it-Q4_K_M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yeoj34760/translategemma-27b-it-Q4_K_M-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
- SGLang
How to use yeoj34760/translategemma-27b-it-Q4_K_M-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 "yeoj34760/translategemma-27b-it-Q4_K_M-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": "yeoj34760/translategemma-27b-it-Q4_K_M-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 "yeoj34760/translategemma-27b-it-Q4_K_M-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": "yeoj34760/translategemma-27b-it-Q4_K_M-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with Ollama:
ollama run hf.co/yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use yeoj34760/translategemma-27b-it-Q4_K_M-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 yeoj34760/translategemma-27b-it-Q4_K_M-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 yeoj34760/translategemma-27b-it-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for yeoj34760/translategemma-27b-it-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use yeoj34760/translategemma-27b-it-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yeoj34760/translategemma-27b-it-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.translategemma-27b-it-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
File size: 2,201 Bytes
d39b02e | 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 | ---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma 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
base_model: google/translategemma-27b-it
tags:
- llama-cpp
- gguf-my-repo
---
# yeoj34760/translategemma-27b-it-Q4_K_M-GGUF
This model was converted to GGUF format from [`google/translategemma-27b-it`](https://huggingface.co/google/translategemma-27b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/google/translategemma-27b-it) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo yeoj34760/translategemma-27b-it-Q4_K_M-GGUF --hf-file translategemma-27b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo yeoj34760/translategemma-27b-it-Q4_K_M-GGUF --hf-file translategemma-27b-it-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo yeoj34760/translategemma-27b-it-Q4_K_M-GGUF --hf-file translategemma-27b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo yeoj34760/translategemma-27b-it-Q4_K_M-GGUF --hf-file translategemma-27b-it-q4_k_m.gguf -c 2048
```
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