Not-For-All-Audiences

Instructions to use ABX-AI/Cerebral-Lemonade-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ABX-AI/Cerebral-Lemonade-9B with Transformers:

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

pipe = pipeline("text-generation", model="ABX-AI/Cerebral-Lemonade-9B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ABX-AI/Cerebral-Lemonade-9B")
model = AutoModelForCausalLM.from_pretrained("ABX-AI/Cerebral-Lemonade-9B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ABX-AI/Cerebral-Lemonade-9B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ABX-AI/Cerebral-Lemonade-9B

SGLang

How to use ABX-AI/Cerebral-Lemonade-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 "ABX-AI/Cerebral-Lemonade-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": "ABX-AI/Cerebral-Lemonade-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 "ABX-AI/Cerebral-Lemonade-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": "ABX-AI/Cerebral-Lemonade-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Cerebral-Lemonade-9B

The concept behind this merge was to use the improved reasoning of of Cerebral-Infinity-7B, and merge it with the improved originality of Infinite-Laymons-7B.

I think the experiment worked, and so far I am happy with the results.

This model is intended for fictional storytelling and role-playing, with a focus on more original conversations and less alignment.

GGUF / IQ / Imatrix

Merge Details

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

Merge Method

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: ABX-AI/Cerebral-Infinity-7B
        layer_range: [0, 20]
  - sources:
      - model: ABX-AI/Infinite-Laymons-7B
        layer_range: [12, 32]
merge_method: passthrough
dtype: float16