Instructions to use burtenshaw/GemmaCoder3-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use burtenshaw/GemmaCoder3-12B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="burtenshaw/GemmaCoder3-12B")
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("burtenshaw/GemmaCoder3-12B")
model = AutoModelForMultimodalLM.from_pretrained("burtenshaw/GemmaCoder3-12B", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use burtenshaw/GemmaCoder3-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "burtenshaw/GemmaCoder3-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "burtenshaw/GemmaCoder3-12B",
		"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/burtenshaw/GemmaCoder3-12B

SGLang

How to use burtenshaw/GemmaCoder3-12B 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 "burtenshaw/GemmaCoder3-12B" \
    --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": "burtenshaw/GemmaCoder3-12B",
		"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 "burtenshaw/GemmaCoder3-12B" \
        --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": "burtenshaw/GemmaCoder3-12B",
		"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 burtenshaw/GemmaCoder3-12B with Docker Model Runner:
```
docker model run hf.co/burtenshaw/GemmaCoder3-12B
```

burtenshaw HF Staff commited on Mar 30, 2025

Commit

c1f688d

verified ·

1 Parent(s): dcbaa9b

End of training

Browse files

Files changed (2) hide show

README.md +3 -3
generation_config.json +4 -2

README.md CHANGED Viewed

@@ -28,15 +28,15 @@ print(output["generated_text"])
 ## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/smartwithfood/huggingface/runs/1du4aw7n)
 This model was trained with SFT.
 ### Framework versions
-- TRL: 0.16.0.dev0
-- Transformers: 4.50.0.dev0
 - Pytorch: 2.6.0
 - Datasets: 3.4.1
 - Tokenizers: 0.21.1

 ## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/smartwithfood/huggingface/runs/mv5o1o6k)
 This model was trained with SFT.
 ### Framework versions
+- TRL: 0.17.0.dev0
+- Transformers: 4.51.0.dev0
 - Pytorch: 2.6.0
 - Datasets: 3.4.1
 - Tokenizers: 0.21.1

generation_config.json CHANGED Viewed

@@ -1,11 +1,13 @@
 {
-  "_from_model_config": true,
   "bos_token_id": 2,
   "cache_implementation": "hybrid",
   "eos_token_id": [
     1,
     106
   ],
   "pad_token_id": 0,
-  "transformers_version": "4.50.0.dev0"
 }

 {
   "bos_token_id": 2,
   "cache_implementation": "hybrid",
+  "do_sample": true,
   "eos_token_id": [
     1,
     106
   ],
   "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.51.0.dev0"
 }