Instructions to use cathuriges/tigerlily-r1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cathuriges/tigerlily-r1.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="cathuriges/tigerlily-r1.1")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("cathuriges/tigerlily-r1.1")
model = AutoModelForMultimodalLM.from_pretrained("cathuriges/tigerlily-r1.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cathuriges/tigerlily-r1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cathuriges/tigerlily-r1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cathuriges/tigerlily-r1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cathuriges/tigerlily-r1.1

SGLang

How to use cathuriges/tigerlily-r1.1 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 "cathuriges/tigerlily-r1.1" \
    --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": "cathuriges/tigerlily-r1.1",
		"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 "cathuriges/tigerlily-r1.1" \
        --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": "cathuriges/tigerlily-r1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cathuriges/tigerlily-r1.1 with Docker Model Runner:
```
docker model run hf.co/cathuriges/tigerlily-r1.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is not a release. The details are my own notes. Don't expect anything from this.

tigerlily-r1_1

This is a merge of pre-trained language models created using mergekit.

Merge Details

Experimental component for a project. Extremely dumb config that I expect to have to tune. Mostly better than R1, less hallucination in vision tasks. Doing TA on the abliterated base seems to restore the refusal vector somewhat. Experimentation is being done to try and transplant abliteration directly, ahead of a Mergekit PR to fix LoRA extraction on Gemma3. Tiger is the domineering "flavor"; could be toned down.

Merge Method

This model was merged using the Task Arithmetic merge method using grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: TheDrummer/Tiger-Gemma-12B-v3
    parameters:
      weight: 1
  - model: soob3123/Veiled-Calla-12B
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated
parameters:
  normalize: true