Instructions to use redrix/GodSlayer-12B-ABYSS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redrix/GodSlayer-12B-ABYSS with Transformers:

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

pipe = pipeline("text-generation", model="redrix/GodSlayer-12B-ABYSS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("redrix/GodSlayer-12B-ABYSS")
model = AutoModelForCausalLM.from_pretrained("redrix/GodSlayer-12B-ABYSS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use redrix/GodSlayer-12B-ABYSS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redrix/GodSlayer-12B-ABYSS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redrix/GodSlayer-12B-ABYSS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/redrix/GodSlayer-12B-ABYSS

SGLang

How to use redrix/GodSlayer-12B-ABYSS 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 "redrix/GodSlayer-12B-ABYSS" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redrix/GodSlayer-12B-ABYSS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "redrix/GodSlayer-12B-ABYSS" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redrix/GodSlayer-12B-ABYSS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use redrix/GodSlayer-12B-ABYSS with Docker Model Runner:
```
docker model run hf.co/redrix/GodSlayer-12B-ABYSS
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GodSlayer-12B-ABYSS

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

The goal of this model is to remain fairly stable and coherent, while counteracting positivity-bias and improving realism and diverse responses.

Model #12

Merge Details

Merge Method

This model was merged using the NuSLERP 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 configurations were used to produce this model:

# P1:
models:
  - model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
    parameters:
      weight:
        - filter: self_attn
          value: 0.2
        - filter: mlp
          value: 0.2
        - value: 0.2
      density: 0.6
  - model: ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2
    parameters:
      weight:
        - filter: self_attn
          value: 0.15
        - filter: mlp
          value: 0.15
        - value: 0.15
      density: 0.55
  - model: HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407
    parameters:
      weight:
        - filter: self_attn
          value: 0.1
        - filter: mlp
          value: 0.1
        - value: 0.1
      density: 0.5
  - model: LatitudeGames/Wayfarer-12B
    parameters:
      weight:
        - filter: self_attn
          value: 0.25
        - filter: mlp
          value: 0.25
        - value: 0.25
      density: 0.65
base_model: TheDrummer/UnslopNemo-12B-v4
merge_method: della_linear
dtype: bfloat16
chat_template: "chatml"
tokenizer_source: union
parameters:
  normalize: true
  int8_mask: true
  epsilon: 0.1
  lambda: 1

# P2:
models:
  - model: rAIfle/Questionable-MN-bf16
    parameters:
      weight:
        - filter: self_attn
          value: 0.2
        - filter: mlp
          value: 0.2
        - value: 0.2
      density: 0.6
  - model: DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS
    parameters:
      weight:
        - filter: self_attn
          value: 0.3
        - filter: mlp
          value: 0.3
        - value: 0.3
      density: 0.7
  - model: allura-org/MN-12b-RP-Ink
    parameters:
      weight:
        - filter: self_attn
          value: 0.35
        - filter: mlp
          value: 0.35
        - value: 0.35
      density: 0.75
  - model: romaingrx/red-teamer-mistral-nemo
    parameters:
      weight:
        - filter: self_attn
          value: 0.25
        - filter: mlp
          value: 0.25
        - value: 0.25
      density: 0.65
base_model: TheDrummer/UnslopNemo-12B-v4
merge_method: della_linear
dtype: bfloat16
chat_template: "chatml"
tokenizer_source: union
parameters:
  normalize: true
  int8_mask: true
  epsilon: 0.1
  lambda: 1

# Final:
models:
  - model: P1
    parameters:
      weight: 0.5
  - model: P2
    parameters:
      weight: 0.5
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
merge_method: nuslerp
dtype: bfloat16
chat_template: "chatml"
tokenizer:
  source: union
parameters:
  normalize: true
  int8_mask: true