Instructions to use BirdToast/gemma4-31b-confetti-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BirdToast/gemma4-31b-confetti-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/aibox/models/gemma-4-31B-pt-embed-it")
model = PeftModel.from_pretrained(base_model, "BirdToast/gemma4-31b-confetti-adapter")

Transformers

How to use BirdToast/gemma4-31b-confetti-adapter with Transformers:

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

pipe = pipeline("text-generation", model="BirdToast/gemma4-31b-confetti-adapter")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BirdToast/gemma4-31b-confetti-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BirdToast/gemma4-31b-confetti-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BirdToast/gemma4-31b-confetti-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdToast/gemma4-31b-confetti-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BirdToast/gemma4-31b-confetti-adapter

SGLang

How to use BirdToast/gemma4-31b-confetti-adapter 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 "BirdToast/gemma4-31b-confetti-adapter" \
    --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": "BirdToast/gemma4-31b-confetti-adapter",
		"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 "BirdToast/gemma4-31b-confetti-adapter" \
        --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": "BirdToast/gemma4-31b-confetti-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BirdToast/gemma4-31b-confetti-adapter with Docker Model Runner:
```
docker model run hf.co/BirdToast/gemma4-31b-confetti-adapter
```

ToastyPigeon commited on May 6

Commit

a2aaa12

verified ·

1 Parent(s): 5d8c660

Upload adapter_config.json with huggingface_hub

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adapter_config.json +38 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,38 @@

+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/home/aibox/models/gemma-4-31B-pt-embed-it",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "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": 8,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": ".*language_model\\.layers\\.\\d+\\.(self_attn\\.(q|k|v|o)_proj|mlp\\.(gate|up|down)_proj)$",
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true
+}