Instructions to use grimjim/Equatorium-v1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/Equatorium-v1-12B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/Equatorium-v1-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/Equatorium-v1-12B")
model = AutoModelForCausalLM.from_pretrained("grimjim/Equatorium-v1-12B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/Equatorium-v1-12B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/grimjim/Equatorium-v1-12B

SGLang

How to use grimjim/Equatorium-v1-12B 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 "grimjim/Equatorium-v1-12B" \
    --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": "grimjim/Equatorium-v1-12B",
		"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 "grimjim/Equatorium-v1-12B" \
        --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": "grimjim/Equatorium-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/Equatorium-v1-12B with Docker Model Runner:
```
docker model run hf.co/grimjim/Equatorium-v1-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Equatorium-v1-12B

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

One of the merge components had refusal partially ablated, then partially healed with a small merge contribution with a model tuned for narrative text completion. The other merge component was a personal preference, with refusals still present. A merge ambition was for the refund of the safety tax in the form of improved reasoning to survive merger and contribute, while avoiding the worst tendencies toward literal repetitions of passages. Sometimes model damage can be leveraged to vary text completion outputs, as damage can function as noise; it's likely not good for benchmarks, in principle, but can reduce the need for samplers that actively suppress passage repetitions.

Merge Details

Merge Method

This model was merged using the Task Arithmetic merge method using grimjim/mistralai-Mistral-Nemo-Base-2407 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: grimjim/mistralai-Mistral-Nemo-Base-2407
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: true
models:
  - model: grimjim/mistralai-Mistral-Nemo-Base-2407
  - model: grimjim/AbMagnolia-v1-12B
    parameters:
      weight: 0.51
  - model: grimjim/Magnolia-v3-12B
    parameters:
      weight: 0.49