Instructions to use ChaoticNeutrals/Infinitely-Laydiculous-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Infinitely-Laydiculous-9B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Infinitely-Laydiculous-9B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Infinitely-Laydiculous-9B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Infinitely-Laydiculous-9B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/Infinitely-Laydiculous-9B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ChaoticNeutrals/Infinitely-Laydiculous-9B

SGLang

How to use ChaoticNeutrals/Infinitely-Laydiculous-9B 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 "ChaoticNeutrals/Infinitely-Laydiculous-9B" \
    --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": "ChaoticNeutrals/Infinitely-Laydiculous-9B",
		"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 "ChaoticNeutrals/Infinitely-Laydiculous-9B" \
        --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": "ChaoticNeutrals/Infinitely-Laydiculous-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Credits to @Lewdiculus for the quants and merge request: https://huggingface.co/Lewdiculous/Infinitely-Laydiculus-9b-GGUF-IQ-Imatrix

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Endevor/InfinityRP-v1-7B
        layer_range: [0, 20]
  - sources:
      - model: l3utterfly/mistral-7b-v0.1-layla-v4
        layer_range: [12, 32]
merge_method: passthrough
dtype: float16

Downloads last month: 9

Safetensors

Model size

9B params

Tensor type

F16

Model tree for ChaoticNeutrals/Infinitely-Laydiculous-9B

Endevor/InfinityRP-v1-7B

l3utterfly/mistral-7b-v0.1-layla-v4

Merge model

this model

Merges

2 models

Quantizations

3 models