Instructions to use Babsie/TaxDocumentBeigePaint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Babsie/TaxDocumentBeigePaint with Transformers:

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

pipe = pipeline("text-generation", model="Babsie/TaxDocumentBeigePaint")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Babsie/TaxDocumentBeigePaint")
model = AutoModelForCausalLM.from_pretrained("Babsie/TaxDocumentBeigePaint", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Babsie/TaxDocumentBeigePaint with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Babsie/TaxDocumentBeigePaint

SGLang

How to use Babsie/TaxDocumentBeigePaint 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 "Babsie/TaxDocumentBeigePaint" \
    --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": "Babsie/TaxDocumentBeigePaint",
		"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 "Babsie/TaxDocumentBeigePaint" \
        --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": "Babsie/TaxDocumentBeigePaint",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

TaxDocumentBeigePaint

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

⚠️ Development Notice – Stage 1 of 3
This is an early-stage merge prototype.
It has only undergone brief testing and exists to verify architecture and tokenizer stability.
Next steps:
2️⃣ Fine-tuning

Use at your own risk 🧌

Merge Details

Merge Method

This model was merged using the TIES merge method using aixonlab/Aether-12b 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: aixonlab/Aether-12b          
    parameters:
      weight: 0.40
  - model: anthracite-org/magnum-v2-12b   
    parameters:
      weight: 0.30
  - model: D1rtyB1rd/Egregore-Alice-RP-NSFW-12B
    parameters:
      weight: 0.15
  - model: nbeerbower/Mistral-Nemo-Gutenberg-Vitus-12B              
    parameters:
      weight: 0.15
merge_method: ties
base_model: aixonlab/Aether-12b
parameters:
  density: 0.45
dtype: float16

🧌 Maintained by: Your Mum
🧠 Variant: Text-only, 12B mistral nemo merge
💾 Upload date: October 2025. TEST Nov 18
☕ Notes: Made with stubbornness, Python, and profanity.