Instructions to use adraganov/gemma-3-12b_custom_code_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use adraganov/gemma-3-12b_custom_code_model with PEFT:
```
Task type is invalid.
```

How to use adraganov/gemma-3-12b_custom_code_model with Transformers:

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

pipe = pipeline("text-generation", model="adraganov/gemma-3-12b_custom_code_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adraganov/gemma-3-12b_custom_code_model")
model = AutoModelForCausalLM.from_pretrained("adraganov/gemma-3-12b_custom_code_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use adraganov/gemma-3-12b_custom_code_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adraganov/gemma-3-12b_custom_code_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adraganov/gemma-3-12b_custom_code_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/adraganov/gemma-3-12b_custom_code_model

SGLang

How to use adraganov/gemma-3-12b_custom_code_model 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 "adraganov/gemma-3-12b_custom_code_model" \
    --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": "adraganov/gemma-3-12b_custom_code_model",
		"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 "adraganov/gemma-3-12b_custom_code_model" \
        --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": "adraganov/gemma-3-12b_custom_code_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use adraganov/gemma-3-12b_custom_code_model with Docker Model Runner:
```
docker model run hf.co/adraganov/gemma-3-12b_custom_code_model
```

gemma-3-12b_custom_code_model

File size: 989 Bytes

43a38dd

{
  "alpha_pattern": {},
  "auto_mapping": {
    "base_model_class": "Gemma3ForCausalLM",
    "parent_library": "transformers.models.gemma3.modeling_gemma3"
  },
  "base_model_name_or_path": "google/gemma-3-12b-it",
  "bias": "none",
  "corda_config": null,
  "eva_config": null,
  "exclude_modules": null,
  "fan_in_fan_out": false,
  "inference_mode": true,
  "init_lora_weights": true,
  "layer_replication": null,
  "layers_pattern": null,
  "layers_to_transform": null,
  "loftq_config": {},
  "lora_alpha": 32,
  "lora_bias": false,
  "lora_dropout": 0.1,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": null,
  "peft_type": "LORA",
  "qalora_group_size": 16,
  "r": 16,
  "rank_pattern": {},
  "revision": null,
  "target_modules": [
    "v_proj",
    "k_proj",
    "q_proj",
    "o_proj"
  ],
  "target_parameters": null,
  "task_type": null,
  "trainable_token_indices": null,
  "use_dora": false,
  "use_qalora": false,
  "use_rslora": false
}