Instructions to use trashpanda-org/gemma-4-31b-larkspur-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trashpanda-org/gemma-4-31b-larkspur-v0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="trashpanda-org/gemma-4-31b-larkspur-v0")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("trashpanda-org/gemma-4-31b-larkspur-v0")
model = AutoModelForMultimodalLM.from_pretrained("trashpanda-org/gemma-4-31b-larkspur-v0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trashpanda-org/gemma-4-31b-larkspur-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trashpanda-org/gemma-4-31b-larkspur-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trashpanda-org/gemma-4-31b-larkspur-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/trashpanda-org/gemma-4-31b-larkspur-v0

SGLang

How to use trashpanda-org/gemma-4-31b-larkspur-v0 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 "trashpanda-org/gemma-4-31b-larkspur-v0" \
    --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": "trashpanda-org/gemma-4-31b-larkspur-v0",
		"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 "trashpanda-org/gemma-4-31b-larkspur-v0" \
        --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": "trashpanda-org/gemma-4-31b-larkspur-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use trashpanda-org/gemma-4-31b-larkspur-v0 with Docker Model Runner:
```
docker model run hf.co/trashpanda-org/gemma-4-31b-larkspur-v0
```

gemma-4-31b-larkspur-v0 / README.md

Hasnonname

revert: remove invalid quantized_versions frontmatter key

82fc6dd verified about 2 months ago

preview code

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metadata

base_model:
  - google/gemma-4-31B
library_name: transformers
tags:
  - gemma4

larkspur

trained from base, nigh-unusable in chat

Quants

Quant	Link
Q8_0	Hasnonname/gemma-4-31b-larkspur-v0-GGUF-Q8_0
Q6_K	Hasnonname/gemma-4-31b-larkspur-v0-GGUF-Q6_K