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 Settings

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
```

GodSlayer-12B-ABYSS / README.md

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	---
	base_model:
	- LatitudeGames/Wayfarer-12B
	- ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2
	- PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
	- HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407
	- TheDrummer/UnslopNemo-12B-v4
	- romaingrx/red-teamer-mistral-nemo
	- DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS
	- rAIfle/Questionable-MN-bf16
	- allura-org/MN-12b-RP-Ink
	- IntervitensInc/Mistral-Nemo-Base-2407-chatml
	library_name: transformers
	tags:
	- mergekit
	- merge
	- 12b
	- chat
	- roleplay
	- creative-writing
	- DELLA-linear

	---
	# GodSlayer-12B-ABYSS

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/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](https://huggingface.co/IntervitensInc/Mistral-Nemo-Base-2407-chatml) as a base.

	### Models Merged

	The following models were included in the merge:
	* [LatitudeGames/Wayfarer-12B](https://huggingface.co/LatitudeGames/Wayfarer-12B)
	* [ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2](https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.2)
	* [PocketDoc/Dans-PersonalityEngine-V1.1.0-12b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.1.0-12b)
	* [HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407](https://huggingface.co/HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407)
	* [romaingrx/red-teamer-mistral-nemo](https://huggingface.co/romaingrx/red-teamer-mistral-nemo)
	* [DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS](https://huggingface.co/DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS)
	* [rAIfle/Questionable-MN-bf16](https://huggingface.co/rAIfle/Questionable-MN-bf16)
	* [allura-org/MN-12b-RP-Ink](https://huggingface.co/allura-org/MN-12b-RP-Ink)

	### Configuration

	The following YAML configurations were used to produce this model:

	```yaml
	# 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
	```

	```yaml
	# 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
	```

	```yaml
	# 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
	```