Instructions to use Vortex5/Chimera-X-26B-A4B-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortex5/Chimera-X-26B-A4B-heretic with Transformers:

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

pipe = pipeline("text-generation", model="Vortex5/Chimera-X-26B-A4B-heretic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Vortex5/Chimera-X-26B-A4B-heretic")
model = AutoModelForMultimodalLM.from_pretrained("Vortex5/Chimera-X-26B-A4B-heretic", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vortex5/Chimera-X-26B-A4B-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Chimera-X-26B-A4B-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortex5/Chimera-X-26B-A4B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortex5/Chimera-X-26B-A4B-heretic

SGLang

How to use Vortex5/Chimera-X-26B-A4B-heretic 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 "Vortex5/Chimera-X-26B-A4B-heretic" \
    --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": "Vortex5/Chimera-X-26B-A4B-heretic",
		"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 "Vortex5/Chimera-X-26B-A4B-heretic" \
        --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": "Vortex5/Chimera-X-26B-A4B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vortex5/Chimera-X-26B-A4B-heretic with Docker Model Runner:
```
docker model run hf.co/Vortex5/Chimera-X-26B-A4B-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a decensored version of Vortex5/Chimera-X-26B-A4B, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method (with row-norm preservation)

Abliteration parameters

Parameter	Value
start_layer_index	6
end_layer_index	23
preserve_good_behavior_weight	0.0473
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	0.7983
neighbor_count	13

Performance

Metric	This model	Original model (Vortex5/Chimera-X-26B-A4B)
KL divergence	0.0392	0 (by definition)
Refusals	12/100	100/100

Chimera-X-26B-A4B

Overview

Chimera-X-26B-A4B was created through a multi-stage merge involving Gemma-4-26B-A4B-Animus-V14.1-FFT, Pantheon-Reasoning-26B-A4B-1.1, G4-MeroMero-26B-A4B, and G4-Moonlight-Dusk-26B-A4B.

Multi-stage merge configuration

name: First
models:
  - model: Gryphe/Pantheon-Reasoning-26B-A4B-1.1
merge_method: nearswap
base_model: Darkhn/Gemma-4-26B-A4B-Animus-V14.1-FFT
parameters:
  t: 0.0008
dtype: float32
out_dtype: bfloat16
---
name: Second
models:
  - model: Vortex5/G4-Moonlight-Dusk-26B-A4B
merge_method: nearswap
chat_template: auto
base_model: zerofata/G4-MeroMero-26B-A4B
parameters:
  t: 0.0008
dtype: float32
out_dtype: bfloat16
---
models:
  - model: First
  - model: Second
merge_method: karcher
chat_template: auto
dtype: float32
out_dtype: bfloat16
parameters:
  tol: 1e-9
  max_iter: 1000
tokenizer:
  source: union

Intended Use

Roleplay

Character interaction, dialogue, personas, and multi-turn scenes.

Creative Writing

Drafting fiction, descriptions, dialogue, and scene variations.

Storytelling

Short stories, longer narratives, plot development, and worldbuilding.

Brainstorming

Generating concepts, outlines, alternatives, and writing ideas.

Downloads last month: 22

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Vortex5/Chimera-X-26B-A4B-heretic

Base model

Vortex5/Chimera-X-26B-A4B

Finetuned

(1)

this model

Quantizations

1 model