Instructions to use Pyroserenus/Orthrus-12b-v0.8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pyroserenus/Orthrus-12b-v0.8 with Transformers:

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

pipe = pipeline("text-generation", model="Pyroserenus/Orthrus-12b-v0.8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pyroserenus/Orthrus-12b-v0.8")
model = AutoModelForCausalLM.from_pretrained("Pyroserenus/Orthrus-12b-v0.8")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Pyroserenus/Orthrus-12b-v0.8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pyroserenus/Orthrus-12b-v0.8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pyroserenus/Orthrus-12b-v0.8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Pyroserenus/Orthrus-12b-v0.8

SGLang

How to use Pyroserenus/Orthrus-12b-v0.8 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 "Pyroserenus/Orthrus-12b-v0.8" \
    --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": "Pyroserenus/Orthrus-12b-v0.8",
		"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 "Pyroserenus/Orthrus-12b-v0.8" \
        --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": "Pyroserenus/Orthrus-12b-v0.8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pyroserenus/Orthrus-12b-v0.8 with Docker Model Runner:
```
docker model run hf.co/Pyroserenus/Orthrus-12b-v0.8
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

o8

This model was meant as a component model for a larger merge but is usable as is.

ChatML prefered, works with Mistral Instruct, kinda works with everything

Tested using 0.75 temp, 0.10 MinP, 0.30 Smoothing Factor

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

Merge Details

Merge Method

This model was merged using the TIES merge method using IntervitensInc/Mistral-Nemo-Base-2407-chatml 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: IntervitensInc/Mistral-Nemo-Base-2407-chatml
  - model: unsloth/Mistral-Nemo-Instruct-2407
    parameters:
      weight: [0.50, 0.20]
      density: [0.75, 0.55]
  - model: anthracite-org/magnum-12b-v2
    parameters:
      weight: [0.50, 0.80]
      density: [0.75, 0.85]
merge_method: ties
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
parameters:
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16