Instructions to use grimjim/Equatorium-v2-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/Equatorium-v2-12B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/Equatorium-v2-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/Equatorium-v2-12B")
model = AutoModelForCausalLM.from_pretrained("grimjim/Equatorium-v2-12B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/Equatorium-v2-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/Equatorium-v2-12B"
# 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-v2-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/Equatorium-v2-12B

SGLang

How to use grimjim/Equatorium-v2-12B 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-v2-12B" \
    --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-v2-12B",
		"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-v2-12B" \
        --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-v2-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/Equatorium-v2-12B with Docker Model Runner:
```
docker model run hf.co/grimjim/Equatorium-v2-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

grimjim/Equatorium-v2-12B

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

I wasn't satisfied with the prompt attention in Equatorium-v1-12B. I attempted repair by going back to original Instruct for the first 10 and final 4 layers, with the goal of improving/restoring overall attention and subsequent coherence. It turned out that the mergekit implementation enabled this by allowing layerwise specification of model weighting. The result in testing (temp=1, minP=0.02) seems sufficiently coherent and varied.

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
  - layer_range: [0, 10]
    model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
    parameters:
      weight: 1.00
  - layer_range: [10, 36]
    model: grimjim/AbMagnolia-v1-12B
    parameters:
      weight: 0.75
  - layer_range: [8, 36]
    model: grimjim/Magnolia-v3-12B
    parameters:
      weight: 0.25
  - layer_range: [36, 40]
    model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
    parameters:
      weight: 1.00