Instructions to use CocoRoF/gemma-3-27b-r1-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CocoRoF/gemma-3-27b-r1-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="CocoRoF/gemma-3-27b-r1-test") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("CocoRoF/gemma-3-27b-r1-test") model = AutoModelForMultimodalLM.from_pretrained("CocoRoF/gemma-3-27b-r1-test") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CocoRoF/gemma-3-27b-r1-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CocoRoF/gemma-3-27b-r1-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CocoRoF/gemma-3-27b-r1-test", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/CocoRoF/gemma-3-27b-r1-test
- SGLang
How to use CocoRoF/gemma-3-27b-r1-test 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 "CocoRoF/gemma-3-27b-r1-test" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CocoRoF/gemma-3-27b-r1-test", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "CocoRoF/gemma-3-27b-r1-test" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CocoRoF/gemma-3-27b-r1-test", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use CocoRoF/gemma-3-27b-r1-test with Docker Model Runner:
docker model run hf.co/CocoRoF/gemma-3-27b-r1-test
| FROM /models/huggingface/CocoRoF__gemma-3-27b-r1-test | |
| TEMPLATE """{{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 }} | |
| {{- if or (eq .Role "user") (eq .Role "system") }}<start_of_turn>user | |
| {{ .Content }}<end_of_turn> | |
| {{ if $last }}<start_of_turn>model | |
| {{ end }} | |
| {{- else if eq .Role "assistant" }}<start_of_turn>model | |
| {{ .Content }}{{ if not $last }}<end_of_turn> | |
| {{ end }} | |
| {{- end }} | |
| {{- end }}""" | |
| PARAMETER stop "<end_of_turn>" | |
| PARAMETER temperature 1 | |
| PARAMETER top_k 64 | |
| PARAMETER top_p 0.95 | |
| SYSTEM """You are a highly capable assistant, POLAR(폴라). For every user question, follow these instructions exactly: | |
| 1. First, think through the problem step-by-step in English. Enclose all of your internal reasoning between <think> and </think> tags. This chain-of-thought should detail your reasoning process. | |
| 2. After the closing </think> tag, provide your final answer. | |
| 3. Do not include any additional text or commentary outside of this format. | |
| 4. Your output should strictly follow this structure: | |
| <think> | |
| [Your detailed step-by-step reasoning in English] | |
| </think> | |
| <solution> | |
| [Your final answer] | |
| </solution>""" |