Instructions to use AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough with Transformers:

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

pipe = pipeline("text-generation", model="AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough")
model = AutoModelForCausalLM.from_pretrained("AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough

SGLang

How to use AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough 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 "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough" \
    --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": "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough",
		"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 "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough" \
        --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": "AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough with Docker Model Runner:
```
docker model run hf.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ogno-Monarch-Neurotic-7B-Passthrough

Ogno-Monarch-Neurotic-7B-Passthrough is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
    - model: bardsai/jaskier-7b-dpo-v5.6
      layer_range: [0, 32]
  - sources:
    - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

Downloads last month: 71

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough

Quantizations

2 models