Instructions to use ohyeah1/Violet-Lyra-Gutenberg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ohyeah1/Violet-Lyra-Gutenberg with Transformers:

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

pipe = pipeline("text-generation", model="ohyeah1/Violet-Lyra-Gutenberg")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ohyeah1/Violet-Lyra-Gutenberg")
model = AutoModelForCausalLM.from_pretrained("ohyeah1/Violet-Lyra-Gutenberg", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ohyeah1/Violet-Lyra-Gutenberg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ohyeah1/Violet-Lyra-Gutenberg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ohyeah1/Violet-Lyra-Gutenberg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ohyeah1/Violet-Lyra-Gutenberg

SGLang

How to use ohyeah1/Violet-Lyra-Gutenberg 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 "ohyeah1/Violet-Lyra-Gutenberg" \
    --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": "ohyeah1/Violet-Lyra-Gutenberg",
		"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 "ohyeah1/Violet-Lyra-Gutenberg" \
        --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": "ohyeah1/Violet-Lyra-Gutenberg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ohyeah1/Violet-Lyra-Gutenberg with Docker Model Runner:
```
docker model run hf.co/ohyeah1/Violet-Lyra-Gutenberg
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Use ChatML or MistralNemo format.

Conclusion: These types of merge methods tend to work better when at least 1 model has a much higher weight then the rest

After further testing this is the best Nemo model I have ever used

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistral-nemo-gutenberg-12B-v4
    parameters:
      weight: 0.2
  - model: Violet_Twilight-v0.2
    parameters:
      weight: 0.3
  - model: Lyra-Gutenberg-mistral-nemo-12B
    parameters:
      weight: 0.5
  - model: Grey-12b
    parameters:
      weight: 0.2
base_model: Mistral-Nemo-Base-2407
parameters:
  density: 0.5
  epsilon: 0.1
  lambda: 1.1
  normalize: false
  int8_mask: true
  rescale: true
merge_method: della_linear
tokenizer:
  source: union
dtype: bfloat16

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for ohyeah1/Violet-Lyra-Gutenberg

Quantizations

2 models